{smcl}
{txt}{sf}{ul off}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\OUTPUT\Performance Management.MANUSCRIPT MODELS.04-10-2025.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}10 Apr 2025, 10:56:12
{txt}
{com}. 
. 
. 
. 
. **** OPEN STATISTICAL DATABASE FILE [07-10-2024]  *****
. 
. use "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\DATA\Performance Management.STATISTICAL DATABASE.07-10-2024.dta", replace
{txt}
{com}. 
. 
. 
. *** SET DATA TO PANEL STRUCTURE  ***
. 
. xtset stateid monthyear, monthly
{res}
{col 1}{txt:Panel variable: }{res:stateid}{txt: (unbalanced)}
{p 1 16 2}{txt:Time variable: }{res:monthyear}{txt:, }{res:{bind:2002m1}}{txt: to }{res:{bind:2022m9}}{p_end}
{txt}{col 10}Delta: {res}1 month
{txt}
{com}. 
. *
. *
. *
. *
. 
. 
. ** GENERATE NATURAL LOGARITHM VERSIONS OF function_sup_avgsalreal [AVERAGE REAL SALARY IN UIP AGENCY MANAGERIAL POSITIONS] & uiadmin_budget_real [REAL AGENCY BUDGET] **
. 
. gen ln_function_sup_avgsalreal = ln(function_sup_avgsalreal)
{txt}
{com}. *
. gen ln_uiadmin_budget_real = ln(uiadmin_budget_real)
{txt}
{com}. 
. 
. 
. 
. *****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. *** COMPUTE CATEGORICAL TASK COMPLEXITY COVARIATE MEASURES [CONDITIONAL ADAPTATION TO IT MODERNIZATION REFORMS] ***
. 
. ** PURPOSE: COMPUTE MARGINAL DIFFERENTIAL EFFECTS IN MANUSCRIPT MODELS [BASED ON EFFECTIVE SAMPLE OF OBSERVATIONS] **
. 
. 
. 
. ** (1) INTERSTATE CASE RATES [PAID & DENIED CLAIMS SAMPLES: TOTAL ERROR RATE & RELATIVE TYPE I ERROR RATE [MODELS 1 & 3];  PAID CLAIMS SAMPLE ONLY: ABSOLUTE TYPE I ERROR RATE [MODEL 2] **
. 
. 
. * Overall Program Error Rate *
. 
. quietly reg totalerror_rat  itmod_monthcount  tot_interstate    tot_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat  i.stateid i.year adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt  adoptcohort_2020_itadopt  adoptcohort_2021_itadopt  if itmod_adopt_state==1
{txt}
{com}. *
. *
. sum tot_interstate if e(sample), detail

                       {txt}tot_interstate
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res} .0222222              0       {txt}Obs         {res}      6,999
{txt}25%    {res} .0444444              0       {txt}Sum of wgt. {res}      6,999

{txt}50%    {res} .0833333                      {txt}Mean          {res} .1094287
                        {txt}Largest       Std. dev.     {res} .1026347
{txt}75%    {res} .1428571       .7738096
{txt}90%    {res} .2222222       .7785548       {txt}Variance      {res} .0105339
{txt}95%    {res} .2987805        .781746       {txt}Skewness      {res}  2.31643
{txt}99%    {res} .5460318       .7857143       {txt}Kurtosis      {res} 10.77513
{txt}
{com}. di r(p75)
{res}.14285715
{txt}
{com}. di r(p25)
{res}.04444445
{txt}
{com}. *
. gen tot_interstate_cat =.
{txt}(12,551 missing values generated)

{com}. replace tot_interstate_cat = 0 if tot_interstate<= r(p25) 
{txt}(2,546 real changes made)

{com}. replace tot_interstate_cat = 1 if tot_interstate> r(p25) & tot_interstate < r(p75) 
{txt}(5,541 real changes made)

{com}. replace tot_interstate_cat = 2 if tot_interstate>= r(p75) 
{txt}(4,464 real changes made)

{com}. *
. tab tot_interstate_cat

{txt}tot_interst {c |}
    ate_cat {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      2,546       20.29       20.29
{txt}          1 {c |}{res}      5,541       44.15       64.43
{txt}          2 {c |}{res}      4,464       35.57      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     12,551      100.00
{txt}
{com}. tab tot_interstate_cat if itmod_adopt_state==1

{txt}tot_interst {c |}
    ate_cat {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,824       24.70       24.70
{txt}          1 {c |}{res}      3,425       46.37       71.07
{txt}          2 {c |}{res}      2,137       28.93      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      7,386      100.00
{txt}
{com}. 
. *
. *
. *
. 
. * Absolute Type I Error Rate *
. 
. quietly reg t1error_rat  itmod_monthcount  t1_interstate    t1_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  t1_totalnonwhite_rat t1_totalfemale_rat t1_totalageu25o65_rat  i.stateid i.year adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt  adoptcohort_2020_itadopt  adoptcohort_2021_itadopt  if itmod_adopt_state==1
{txt}
{com}. *
. *
. sum t1_interstate if e(sample), detail

                        {txt}t1_interstate
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      7,262
{txt}25%    {res}      .02              0       {txt}Sum of wgt. {res}      7,262

{txt}50%    {res}      .04                      {txt}Mean          {res} .0538885
                        {txt}Largest       Std. dev.     {res} .0574815
{txt}75%    {res}     .075       .4117647
{txt}90%    {res}     .125       .4222222       {txt}Variance      {res} .0033041
{txt}95%    {res} .1627907       .4285714       {txt}Skewness      {res}  2.06627
{txt}99%    {res} .2857143       .4285714       {txt}Kurtosis      {res}  9.28193
{txt}
{com}. di r(p75)
{res}.075
{txt}
{com}. di r(p25)
{res}.02
{txt}
{com}. *
. gen t1_interstate_cat =.
{txt}(12,551 missing values generated)

{com}. replace t1_interstate_cat = 0 if t1_interstate<= r(p25) 
{txt}(2,703 real changes made)

{com}. replace t1_interstate_cat = 1 if t1_interstate> r(p25) & t1_interstate < r(p75) 
{txt}(5,647 real changes made)

{com}. replace t1_interstate_cat = 2 if t1_interstate>= r(p75) 
{txt}(4,201 real changes made)

{com}. *
. tab t1_interstate_cat

{txt}t1_intersta {c |}
     te_cat {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      2,703       21.54       21.54
{txt}          1 {c |}{res}      5,647       44.99       66.53
{txt}          2 {c |}{res}      4,201       33.47      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     12,551      100.00
{txt}
{com}. tab t1_interstate_cat if itmod_adopt_state==1

{txt}t1_intersta {c |}
     te_cat {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,863       25.22       25.22
{txt}          1 {c |}{res}      3,495       47.32       72.54
{txt}          2 {c |}{res}      2,028       27.46      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      7,386      100.00
{txt}
{com}. *
. *
. *
. 
. * Relative Type I Error Rate [same as Overall Program Error Rate since Contains Both Type I & Type II Program Error Rates] *
. 
. quietly reg relt1error_rat  itmod_monthcount  tot_interstate    tot_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat  i.stateid i.year adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt  adoptcohort_2020_itadopt  adoptcohort_2021_itadopt  if itmod_adopt_state==1
{txt}
{com}. *
. *
. sum tot_interstate if e(sample), detail

                       {txt}tot_interstate
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res} .0222222              0       {txt}Obs         {res}      6,770
{txt}25%    {res} .0444444              0       {txt}Sum of wgt. {res}      6,770

{txt}50%    {res} .0833333                      {txt}Mean          {res} .1088127
                        {txt}Largest       Std. dev.     {res} .1013154
{txt}75%    {res} .1425121       .7738096
{txt}90%    {res} .2222222       .7785548       {txt}Variance      {res} .0102648
{txt}95%    {res} .2928572        .781746       {txt}Skewness      {res} 2.312054
{txt}99%    {res} .5392157       .7857143       {txt}Kurtosis      {res} 10.82831
{txt}
{com}. di r(p75)
{res}.14251208
{txt}
{com}. di r(p25)
{res}.04444445
{txt}
{com}. *
. gen relt1_interstate_cat =.
{txt}(12,551 missing values generated)

{com}. replace relt1_interstate_cat = 0 if tot_interstate<= r(p25) 
{txt}(2,546 real changes made)

{com}. replace relt1_interstate_cat = 1 if tot_interstate> r(p25) & tot_interstate < r(p75) 
{txt}(5,535 real changes made)

{com}. replace relt1_interstate_cat = 2 if tot_interstate>= r(p75) 
{txt}(4,470 real changes made)

{com}. *
. tab relt1_interstate_cat

{txt}relt1_inter {c |}
  state_cat {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      2,546       20.29       20.29
{txt}          1 {c |}{res}      5,535       44.10       64.39
{txt}          2 {c |}{res}      4,470       35.61      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     12,551      100.00
{txt}
{com}. tab relt1_interstate_cat if itmod_adopt_state==1

{txt}relt1_inter {c |}
  state_cat {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,824       24.70       24.70
{txt}          1 {c |}{res}      3,419       46.29       70.99
{txt}          2 {c |}{res}      2,143       29.01      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      7,386      100.00
{txt}
{com}. *
. 
. 
. 
. *****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. ** (2) DIFFERENT OCCUPATION SEEKING RATE [PAID & DENIED CLAIMS SAMPLES: TOTAL ERROR RATE & RELATIVE TYPE I ERROR RATE [MODELS 1 & 3];  PAID CLAIMS SAMPLE ONLY: ABSOLUTE TYPE I ERROR RATE [MODEL 2] **
. 
. 
. * Overall Program Error Rate *
. 
. quietly reg totalerror_rat  itmod_monthcount  tot_interstate    tot_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat  i.stateid i.year  adoptcohort_2002_itadopt  adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt  adoptcohort_2020_itadopt  adoptcohort_2021_itadopt  if itmod_adopt_state==1
{txt}
{com}. 
. sum tot_diffoccupseek if e(sample), detail

                      {txt}tot_diffoccupseek
{hline 61}
      Percentiles      Smallest
 1%    {res} .1303419              0
{txt} 5%    {res} .2142857              0
{txt}10%    {res} .2777778              0       {txt}Obs         {res}      6,999
{txt}25%    {res} .3944445              0       {txt}Sum of wgt. {res}      6,999

{txt}50%    {res} .5416667                      {txt}Mean          {res} .5463753
                        {txt}Largest       Std. dev.     {res} .2080455
{txt}75%    {res} .6877193       1.207547
{txt}90%    {res} .8250751       1.219512       {txt}Variance      {res} .0432829
{txt}95%    {res} .9024024       1.299107       {txt}Skewness      {res}  .199589
{txt}99%    {res} 1.051282       1.299107       {txt}Kurtosis      {res} 2.712532
{txt}
{com}. di r(p75)
{res}.68771935
{txt}
{com}. di r(p25)
{res}.39444447
{txt}
{com}. *
. gen tot_diffoccupseek_cat =.
{txt}(12,551 missing values generated)

{com}. replace tot_diffoccupseek_cat = 0 if tot_diffoccupseek<= r(p25) 
{txt}(3,122 real changes made)

{com}. replace tot_diffoccupseek_cat = 1 if tot_diffoccupseek> r(p25) & tot_diffoccupseek < r(p75) 
{txt}(5,991 real changes made)

{com}. replace tot_diffoccupseek_cat = 2 if tot_diffoccupseek>= r(p75) 
{txt}(3,438 real changes made)

{com}. *
. tab tot_diffoccupseek_cat

{txt}tot_diffocc {c |}
 upseek_cat {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      3,122       24.87       24.87
{txt}          1 {c |}{res}      5,991       47.73       72.61
{txt}          2 {c |}{res}      3,438       27.39      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     12,551      100.00
{txt}
{com}. tab tot_diffoccupseek_cat if itmod_adopt_state==1

{txt}tot_diffocc {c |}
 upseek_cat {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,751       23.71       23.71
{txt}          1 {c |}{res}      3,499       47.37       71.08
{txt}          2 {c |}{res}      2,136       28.92      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      7,386      100.00
{txt}
{com}. 
. *
. *
. *
. 
. * Absolute Type I Error Rate *
. 
. quietly reg t1error_rat  itmod_monthcount  t1_interstate    t1_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  t1_totalnonwhite_rat t1_totalfemale_rat t1_totalageu25o65_rat  i.stateid i.year  adoptcohort_2002_itadopt  adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt  adoptcohort_2020_itadopt  adoptcohort_2021_itadopt  if itmod_adopt_state==1
{txt}
{com}. *
. *
. sum t1_diffoccupseek if e(sample), detail

                      {txt}t1_diffoccupseek
{hline 61}
      Percentiles      Smallest
 1%    {res} .0444444              0
{txt} 5%    {res} .0967742              0
{txt}10%    {res}     .125              0       {txt}Obs         {res}      7,262
{txt}25%    {res} .1891892              0       {txt}Sum of wgt. {res}      7,262

{txt}50%    {res}      .25                      {txt}Mean          {res} .2610355
                        {txt}Largest       Std. dev.     {res} .1070272
{txt}75%    {res}  .326087       .7142857
{txt}90%    {res}       .4       .7142857       {txt}Variance      {res} .0114548
{txt}95%    {res} .4444444       .8214286       {txt}Skewness      {res} .3650516
{txt}99%    {res} .5357143       .8214286       {txt}Kurtosis      {res} 3.252303
{txt}
{com}. di r(p75)
{res}.32608697
{txt}
{com}. di r(p25)
{res}.1891892
{txt}
{com}. *
. gen t1_diffoccupseek_cat =.
{txt}(12,551 missing values generated)

{com}. replace t1_diffoccupseek_cat = 0 if t1_diffoccupseek<= r(p25) 
{txt}(3,675 real changes made)

{com}. replace t1_diffoccupseek_cat = 1 if t1_diffoccupseek> r(p25) & t1_diffoccupseek < r(p75) 
{txt}(5,881 real changes made)

{com}. replace t1_diffoccupseek_cat = 2 if t1_diffoccupseek>= r(p75) 
{txt}(2,995 real changes made)

{com}. *
. tab t1_diffoccupseek_cat

{txt}t1_diffoccu {c |}
  pseek_cat {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      3,675       29.28       29.28
{txt}          1 {c |}{res}      5,881       46.86       76.14
{txt}          2 {c |}{res}      2,995       23.86      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     12,551      100.00
{txt}
{com}. tab t1_diffoccupseek_cat if itmod_adopt_state==1

{txt}t1_diffoccu {c |}
  pseek_cat {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,862       25.21       25.21
{txt}          1 {c |}{res}      3,574       48.39       73.60
{txt}          2 {c |}{res}      1,950       26.40      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      7,386      100.00
{txt}
{com}. *
. *
. *
. 
. * Relative Type I Error Rate [same as Overall Program Error Rate since Contains Both Type I & Type II Program Error Rates] *
. 
. quietly reg relt1error_rat  itmod_monthcount  tot_interstate    tot_diffoccupseek demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat  i.stateid i.year   adoptcohort_2002_itadopt  adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt  adoptcohort_2020_itadopt  adoptcohort_2021_itadopt  if itmod_adopt_state==1
{txt}
{com}. *
. *
. sum tot_diffoccupseek if e(sample), detail

                      {txt}tot_diffoccupseek
{hline 61}
      Percentiles      Smallest
 1%    {res} .1358859       .0181818
{txt} 5%    {res} .2166667       .0277778
{txt}10%    {res} .2777778       .0527778       {txt}Obs         {res}      6,770
{txt}25%    {res} .3956522       .0548048       {txt}Sum of wgt. {res}      6,770

{txt}50%    {res} .5435515                      {txt}Mean          {res} .5481698
                        {txt}Largest       Std. dev.     {res} .2075155
{txt}75%    {res} .6891892       1.207547
{txt}90%    {res} .8279963       1.219512       {txt}Variance      {res} .0430627
{txt}95%    {res} .9047619       1.299107       {txt}Skewness      {res} .2191119
{txt}99%    {res} 1.051724       1.299107       {txt}Kurtosis      {res} 2.680501
{txt}
{com}. di r(p75)
{res}.6891892
{txt}
{com}. di r(p25)
{res}.39565217
{txt}
{com}. *
. gen relt1_diffoccupseek_cat =.
{txt}(12,551 missing values generated)

{com}. replace relt1_diffoccupseek_cat = 0 if tot_diffoccupseek<= r(p25) 
{txt}(3,137 real changes made)

{com}. replace relt1_diffoccupseek_cat = 1 if tot_diffoccupseek> r(p25) & tot_diffoccupseek < r(p75) 
{txt}(6,005 real changes made)

{com}. replace relt1_diffoccupseek_cat = 2 if tot_diffoccupseek>= r(p75) 
{txt}(3,409 real changes made)

{com}. *
. tab relt1_diffoccupseek_cat

{txt}relt1_diffo {c |}
ccupseek_ca {c |}
          t {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      3,137       24.99       24.99
{txt}          1 {c |}{res}      6,005       47.84       72.84
{txt}          2 {c |}{res}      3,409       27.16      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     12,551      100.00
{txt}
{com}. tab relt1_diffoccupseek_cat if itmod_adopt_state==1

{txt}relt1_diffo {c |}
ccupseek_ca {c |}
          t {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,763       23.87       23.87
{txt}          1 {c |}{res}      3,502       47.41       71.28
{txt}          2 {c |}{res}      2,121       28.72      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      7,386      100.00
{txt}
{com}. 
. 
. **************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
.  
. 
. *** SAVE MANUSCRIPT DATABASE [as of 04-10-2025] ***
. 
. 
. save "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\DATA\Performance Management.MANUSCRIPT DATABASE.04-10-2025.dta", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\DATA\Performance Management.MANUSCRIPT DATABASE.04-10-2025.dta{rm}
not found)
{p_end}
{p 0 4 2}
file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\DATA\Performance Management.MANUSCRIPT DATABASE.04-10-2025.dta{rm}
saved
{p_end}

{com}. 
. 
. 
. 
. *** MANUSCRIPT MODELS [TABLE 1; FIGURES 3-5] ***
. 
. 
. 
. 
. 
. ********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. 
. *** MODELS PREDICTING VARIOPUS TYPE OF PROGRAM ERROR RATES BASED ON BAM SAMPLING RATES ***
. 
. 
. 
. 
. 
. *** MODEL 1: OVERALL ERROR RATE: (SAMPLE WEIGHTED) ***
. 
. *  [# overpayment errors / paid claims sample] + [# underpayment errors / paid claims sample] + [# erroneous denials / denied claims sample] + [# underpayment errors / denied claims sample] *
. 
. 
. 
. *** MODEL 2: ABSOLUTE TYPE I ERROR RATE ***
. 
. * [overpayment error rate / paid claims sample] *
. 
. 
. 
. 
. *** MODEL 3: RELATIVE TYPE I ERROR RATE:  {c -(}TYPE I ERROR RATE /  [TYPE I ERROR RATE + TYPE II ERROR RATE]{c )-}      (SAMPLE WEIGHTED)  ***
. 
. *  {c -(}[overpayment error rate / paid claims sample]   /  [overpayment error rate / paid claims sample]   +  [underpayment error rate / paid claims sample]  +  [erroneous denial / denied claims sample]  +  [underpayment error / denied claims sample]{c )-}  *
. 
. 
. 
. 
. 
. 
. ********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. 
. *** RETRIEVE MANUSCRIPT DATABASE [as of 04-10-2025] ***
. 
. use "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\DATA\Performance Management.MANUSCRIPT DATABASE.04-10-2025.dta", replace
{txt}
{com}. 
. 
. 
. 
. *** SET DATA TO PANEL STRUCTURE  ***
. 
. xtset stateid monthyear, monthly
{res}
{col 1}{txt:Panel variable: }{res:stateid}{txt: (unbalanced)}
{p 1 16 2}{txt:Time variable: }{res:monthyear}{txt:, }{res:{bind:2002m1}}{txt: to }{res:{bind:2022m9}}{p_end}
{txt}{col 10}Delta: {res}1 month
{txt}
{com}. 
. *
. *
. *
. *
. 
. 
. 
. ********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. *** DESCRIPTIVE STATISTICS FOR DEPENDENT VARIABLE BASED ON EFFECTIVE REGRESSION SAMPLE [PLEASE NOTE: ALL DESCRIPTIVE STATISTICS ARE BASED ON OVERALL PROGRAM ERROR RATE SAMPLE OF OBSERVATIONS: N*T = 7,000] ***
. 
. 
. 
. ** DEPENDENT VARIABLES **
. 
. sum totalerror_rat t1error_rat  relt1error_rat  if itmod_adopt_state==1 & !missing(totalerror_rat), detail

                       {txt}totalerror_rat
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}     .025              0
{txt}10%    {res}   .04381              0       {txt}Obs         {res}      7,000
{txt}25%    {res} .0833333              0       {txt}Sum of wgt. {res}      7,000

{txt}50%    {res} .1388889                      {txt}Mean          {res}  .172483
                        {txt}Largest       Std. dev.     {res} .1340822
{txt}75%    {res} .2222222       .9333334
{txt}90%    {res} .3361111           .975       {txt}Variance      {res}  .017978
{txt}95%    {res} .4295635              1       {txt}Skewness      {res} 1.780748
{txt}99%    {res} .6666667       1.055556       {txt}Kurtosis      {res} 7.549466

                         {txt}t1error_rat
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      7,000
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      7,000

{txt}50%    {res} .0285714                      {txt}Mean          {res} .0540814
                        {txt}Largest       Std. dev.     {res} .0812379
{txt}75%    {res} .0638298       .6315789
{txt}90%    {res} .1190476       .6346154       {txt}Variance      {res} .0065996
{txt}95%    {res}       .2       .6410257       {txt}Skewness      {res} 3.401451
{txt}99%    {res} .4666667       .6666667       {txt}Kurtosis      {res} 17.29403

                       {txt}relt1error_rat
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      6,771
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      6,771

{txt}50%    {res}  .244898                      {txt}Mean          {res} .2854898
                        {txt}Largest       Std. dev.     {res} .2619357
{txt}75%    {res} .4615384              1
{txt}90%    {res} .6545454              1       {txt}Variance      {res} .0686103
{txt}95%    {res} .7927461              1       {txt}Skewness      {res} .8435258
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 3.149101
{txt}
{com}. 
. 
. 
. ** ORGANIZATIONAL ADAPTATION COVARIATE **
. 
. sum itmod_monthcount if itmod_adopt_state==1 & !missing(totalerror_rat), detail

                      {txt}itmod_monthcount
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      7,000
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      7,000

{txt}50%    {res}        0                      {txt}Mean          {res} 25.05457
                        {txt}Largest       Std. dev.     {res} 44.79909
{txt}75%    {res}       35            233
{txt}90%    {res}       95            234       {txt}Variance      {res} 2006.959
{txt}95%    {res}    130.5            235       {txt}Skewness      {res} 2.022593
{txt}99%    {res}      186            236       {txt}Kurtosis      {res} 6.613519
{txt}
{com}. 
. 
. 
. ** CONTROL COVARIATES [EXCLUDING UNIT EFFECTS] *** tot: Models 1, 3, and 4// t1: Model 2 (Type I)
. 
. sum demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat t1_totalnonwhite_rat t1_totalfemale_rat t1_totalageu25o65_rat if itmod_adopt_state==1  & !missing(totalerror_rat), detail

                         {txt}demgovparty
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      7,000
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      7,000

{txt}50%    {res}        0                      {txt}Mean          {res} .4028571
                        {txt}Largest       Std. dev.     {res} .4905075
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .2405976
{txt}95%    {res}        1              1       {txt}Skewness      {res} .3961195
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 1.156911

                         {txt}repgovparty
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      7,000
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      7,000

{txt}50%    {res}        1                      {txt}Mean          {res} .5897143
                        {txt}Largest       Std. dev.     {res} .4919206
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .2419859
{txt}95%    {res}        1              1       {txt}Skewness      {res}-.3647771
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 1.133062

                         {txt}ln_workload
{hline 61}
      Percentiles      Smallest
 1%    {res} 6.313548       2.833213
{txt} 5%    {res} 6.992095       5.620401
{txt}10%    {res} 7.395414       5.703783       {txt}Obs         {res}      7,000
{txt}25%    {res} 8.051181       5.736572       {txt}Sum of wgt. {res}      7,000

{txt}50%    {res} 8.840724                      {txt}Mean          {res} 8.846052
                        {txt}Largest       Std. dev.     {res} 1.136745
{txt}75%    {res} 9.644263       11.96257
{txt}90%    {res}  10.2889       11.97578       {txt}Variance      {res}  1.29219
{txt}95%    {res} 10.74098        11.9999       {txt}Skewness      {res} .0354381
{txt}99%    {res} 11.50401        12.2323       {txt}Kurtosis      {res} 2.817835

                       {txt}automationrate
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res} .0238095              0       {txt}Obs         {res}      7,000
{txt}25%    {res}      .24              0       {txt}Sum of wgt. {res}      7,000

{txt}50%    {res} .5384616                      {txt}Mean          {res} .5110405
                        {txt}Largest       Std. dev.     {res} .3169968
{txt}75%    {res} .7541161              1
{txt}90%    {res} .9658095              1       {txt}Variance      {res}  .100487
{txt}95%    {res}        1              1       {txt}Skewness      {res}-.1137005
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 1.885214

                   {txt}ln_uiadmin_budget_real
{hline 61}
      Percentiles      Smallest
 1%    {res} 16.02041       15.76789
{txt} 5%    {res} 16.43352       15.76789
{txt}10%    {res}  16.6406       15.76789       {txt}Obs         {res}      7,000
{txt}25%    {res} 16.94374       15.76789       {txt}Sum of wgt. {res}      7,000

{txt}50%    {res} 17.59898                      {txt}Mean          {res} 17.65519
                        {txt}Largest       Std. dev.     {res} .8893803
{txt}75%    {res} 18.20317       20.41715
{txt}90%    {res} 18.86842       20.41715       {txt}Variance      {res} .7909973
{txt}95%    {res} 19.08243       20.41715       {txt}Skewness      {res} .5527573
{txt}99%    {res} 20.13694       20.41715       {txt}Kurtosis      {res} 3.085808

                      {txt}benefitgenerosity
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res} .1236963              0
{txt}10%    {res} .1749672              0       {txt}Obs         {res}      7,000
{txt}25%    {res} .2607627              0       {txt}Sum of wgt. {res}      7,000

{txt}50%    {res} .3444819                      {txt}Mean          {res} .3426126
                        {txt}Largest       Std. dev.     {res} .1369361
{txt}75%    {res} .4200969       .8328832
{txt}90%    {res} .5107075       .9476796       {txt}Variance      {res} .0187515
{txt}95%    {res} .5808617       .9476796       {txt}Skewness      {res} .0244157
{txt}99%    {res} .6882279       .9476796       {txt}Kurtosis      {res} 3.702307

                         {txt}unemp_rate
{hline 61}
      Percentiles      Smallest
 1%    {res}      2.4            1.8
{txt} 5%    {res}      2.8            1.8
{txt}10%    {res}      3.2            1.8       {txt}Obs         {res}      7,000
{txt}25%    {res}        4            1.8       {txt}Sum of wgt. {res}      7,000

{txt}50%    {res}      5.2                      {txt}Mean          {res} 5.590143
                        {txt}Largest       Std. dev.     {res} 2.146939
{txt}75%    {res}      6.7           14.1
{txt}90%    {res}      8.5           14.6       {txt}Variance      {res} 4.609347
{txt}95%    {res}      9.9           14.6       {txt}Skewness      {res}  .996075
{txt}99%    {res}     12.1           15.9       {txt}Kurtosis      {res} 4.005939

                 {txt}ln_function_sup_avgsalreal
{hline 61}
      Percentiles      Smallest
 1%    {res}  10.4074       10.05185
{txt} 5%    {res} 10.71927       10.11343
{txt}10%    {res} 10.80227       10.11343       {txt}Obs         {res}      7,000
{txt}25%    {res} 10.89391       10.11343       {txt}Sum of wgt. {res}      7,000

{txt}50%    {res} 11.03609                      {txt}Mean          {res} 11.01831
                        {txt}Largest       Std. dev.     {res} .1963653
{txt}75%    {res} 11.15273       11.54676
{txt}90%    {res} 11.25297       11.54676       {txt}Variance      {res} .0385593
{txt}95%    {res} 11.30712       11.54676       {txt}Skewness      {res}-.7250397
{txt}99%    {res} 11.39721       11.54676       {txt}Kurtosis      {res} 4.561152

                    {txt}tot_totalnonwhite_rat
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res} .0666667              0
{txt}10%    {res} .1246499              0       {txt}Obs         {res}      6,999
{txt}25%    {res} .2853682              0       {txt}Sum of wgt. {res}      6,999

{txt}50%    {res}       .5                      {txt}Mean          {res} .6297344
                        {txt}Largest       Std. dev.     {res} .4484937
{txt}75%    {res} .9484321              2
{txt}90%    {res} 1.261905              2       {txt}Variance      {res} .2011466
{txt}95%    {res} 1.516908              2       {txt}Skewness      {res}  .791603
{txt}99%    {res}        2              2       {txt}Kurtosis      {res}  2.94418

                     {txt}tot_totalfemale_rat
{hline 61}
      Percentiles      Smallest
 1%    {res} .5331565            .25
{txt} 5%    {res} .6289593       .2847222
{txt}10%    {res} .6944444       .3089655       {txt}Obs         {res}      6,999
{txt}25%    {res} .7958333       .3293651       {txt}Sum of wgt. {res}      6,999

{txt}50%    {res} .9061562                      {txt}Mean          {res} .9073465
                        {txt}Largest       Std. dev.     {res} .1660691
{txt}75%    {res} 1.018341        1.45614
{txt}90%    {res} 1.123123       1.466151       {txt}Variance      {res} .0275789
{txt}95%    {res} 1.181185       1.481982       {txt}Skewness      {res}-.0021548
{txt}99%    {res} 1.288889            1.5       {txt}Kurtosis      {res} 2.985396

                   {txt}tot_totalageu25o65_rat
{hline 61}
      Percentiles      Smallest
 1%    {res} .1059397       .0238095
{txt} 5%    {res} .1588235           .025
{txt}10%    {res} .1904762       .0277778       {txt}Obs         {res}      6,999
{txt}25%    {res} .2444445       .0277778       {txt}Sum of wgt. {res}      6,999

{txt}50%    {res} .3095238                      {txt}Mean          {res} .3156073
                        {txt}Largest       Std. dev.     {res} .1018059
{txt}75%    {res} .3809524       .7630421
{txt}90%    {res} .4477799           .875       {txt}Variance      {res} .0103644
{txt}95%    {res} .4888889           .875       {txt}Skewness      {res} .3994217
{txt}99%    {res} .5798097       .9248366       {txt}Kurtosis      {res} 3.574767

                    {txt}t1_totalnonwhite_rat
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res} .0227273              0
{txt}10%    {res}      .04              0       {txt}Obs         {res}      7,000
{txt}25%    {res} .1111111              0       {txt}Sum of wgt. {res}      7,000

{txt}50%    {res} .2258064                      {txt}Mean          {res} .2880536
                        {txt}Largest       Std. dev.     {res} .2220246
{txt}75%    {res}      .44              1
{txt}90%    {res}       .6              1       {txt}Variance      {res} .0492949
{txt}95%    {res} .7158385              1       {txt}Skewness      {res} .8932214
{txt}99%    {res}  .972973              1       {txt}Kurtosis      {res} 3.261797

                     {txt}t1_totalfemale_rat
{hline 61}
      Percentiles      Smallest
 1%    {res} .1785714       .0384615
{txt} 5%    {res}      .25       .0555556
{txt}10%    {res} .2888889       .0689655       {txt}Obs         {res}      7,000
{txt}25%    {res} .3571429       .0740741       {txt}Sum of wgt. {res}      7,000

{txt}50%    {res} .4312035                      {txt}Mean          {res}  .432374
                        {txt}Largest       Std. dev.     {res}  .110385
{txt}75%    {res}       .5       .8333333
{txt}90%    {res} .5714286            .84       {txt}Variance      {res} .0121849
{txt}95%    {res} .6111111       .8571429       {txt}Skewness      {res} .0089275
{txt}99%    {res} .6905983       .8888889       {txt}Kurtosis      {res} 2.994177

                    {txt}t1_totalageu25o65_rat
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res} .0357143              0
{txt}10%    {res} .0555556              0       {txt}Obs         {res}      7,000
{txt}25%    {res} .0833333              0       {txt}Sum of wgt. {res}      7,000

{txt}50%    {res} .1219512                      {txt}Mean          {res} .1247855
                        {txt}Largest       Std. dev.     {res} .0565553
{txt}75%    {res} .1612903       .3571429
{txt}90%    {res}       .2       .3666667       {txt}Variance      {res} .0031985
{txt}95%    {res} .2222222            .38       {txt}Skewness      {res} .3767881
{txt}99%    {res} .2666667       .4285714       {txt}Kurtosis      {res} 3.161994
{txt}
{com}. 
. 
. 
. 
. 
. ********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. 
. **** TABLE 1 -- MODELS 1-3: "TASK COMPLEXITY, ORGANIZATIONAL ADAPTATION & PROGRAM ERROR RATES" MANUSCRIPT STATISTICAL ANALYSES [APRIL 2025]: ORGANIZATIONAL ADAPTATION EFFECTS ON PROGRAM PAYMENT ERROR RATES [M1: TOTAL PROGRAM ERROR RATE; M2: ABSOLUTE TYPE I PROGRAM ERROR RATE; M3: RELATIVE TYPE I PROGRAM ERROR RATE] **** 
. 
. 
. ** (MODEL 1; FIGURES 3A-3C; MODEL 2: FIGURES 4A-4C; MODEL 3: FIGURES 4D-4F) **
. 
. 
. 
. 
. ************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. *** TESTING H1 & H3: TOTAL/OVERALL PROGRAM ERROR RATE ORGANIZATIONAL ADAPTATION  ***
. 
. 
. 
. *** ESTIMATE MODEL 1: TOTAL PROGRAM ERROR  RATE [PROPORTION OF SAMPLE-WEIGHTED CASES OF TOTAL ERRORS VIA WEEKLY BAM SURVEY AGGREGATED TO MONTHLY OBSERVATIONS: [CONTROLS, PLUS STATE, YEAR, AND YEAR-ADOPTION COHORT*TREATMENT UNIT EFFECTS] ***        (FIGURES 3A-3C) 
. 
. npregress series totalerror_rat  itmod_monthcount  i.tot_interstate_cat   i.tot_diffoccupseek_cat   if itmod_adopt_state==1, asis(demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat    i.stateid i.year  adoptcohort_2002_itadopt  adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt  adoptcohort_2020_itadopt  adoptcohort_2021_itadopt)  vce(bootstrap, seed(123) rep(1000))
{res}{txt}(running {bf:npregress} on estimation sample)
{res}
{text}Bootstrap replications ({result:1,000}){text}: 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done
{res}
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{txt}{space 23}28  {c |}{col 28}{res}{space 2} .0444425{col 40}{space 2} .0087263{col 51}{space 1}    5.09{col 60}{space 3}0.000{col 68}{space 4} .0273393{col 81}{space 3} .0615457
{txt}{space 23}29  {c |}{col 28}{res}{space 2} .2092609{col 40}{space 2} .0181914{col 51}{space 1}   11.50{col 60}{space 3}0.000{col 68}{space 4} .1736065{col 81}{space 3} .2449153
{txt}{space 23}31  {c |}{col 28}{res}{space 2} .0963818{col 40}{space 2} .0288689{col 51}{space 1}    3.34{col 60}{space 3}0.001{col 68}{space 4} .0397998{col 81}{space 3} .1529638
{txt}{space 23}33  {c |}{col 28}{res}{space 2} -.012466{col 40}{space 2} .0119096{col 51}{space 1}   -1.05{col 60}{space 3}0.295{col 68}{space 4}-.0358085{col 81}{space 3} .0108765
{txt}{space 23}35  {c |}{col 28}{res}{space 2} .0441612{col 40}{space 2} .0253069{col 51}{space 1}    1.75{col 60}{space 3}0.081{col 68}{space 4}-.0054395{col 81}{space 3} .0937618
{txt}{space 23}38  {c |}{col 28}{res}{space 2}  .127615{col 40}{space 2} .0241152{col 51}{space 1}    5.29{col 60}{space 3}0.000{col 68}{space 4} .0803501{col 81}{space 3} .1748798
{txt}{space 23}40  {c |}{col 28}{res}{space 2}  .004889{col 40}{space 2} .0100238{col 51}{space 1}    0.49{col 60}{space 3}0.626{col 68}{space 4}-.0147574{col 81}{space 3} .0245353
{txt}{space 23}42  {c |}{col 28}{res}{space 2} .1494465{col 40}{space 2} .0095511{col 51}{space 1}   15.65{col 60}{space 3}0.000{col 68}{space 4} .1307267{col 81}{space 3} .1681663
{txt}{space 23}44  {c |}{col 28}{res}{space 2} .0787322{col 40}{space 2} .0148105{col 51}{space 1}    5.32{col 60}{space 3}0.000{col 68}{space 4} .0497041{col 81}{space 3} .1077603
{txt}{space 23}46  {c |}{col 28}{res}{space 2} .0590811{col 40}{space 2} .0115712{col 51}{space 1}    5.11{col 60}{space 3}0.000{col 68}{space 4}  .036402{col 81}{space 3} .0817602
{txt}{space 23}47  {c |}{col 28}{res}{space 2}-.0409863{col 40}{space 2} .0159534{col 51}{space 1}   -2.57{col 60}{space 3}0.010{col 68}{space 4}-.0722544{col 81}{space 3}-.0097182
{txt}{space 23}50  {c |}{col 28}{res}{space 2}  .163699{col 40}{space 2} .0236774{col 51}{space 1}    6.91{col 60}{space 3}0.000{col 68}{space 4} .1172921{col 81}{space 3} .2101059
{txt}{space 23}51  {c |}{col 28}{res}{space 2} .1941714{col 40}{space 2} .0156703{col 51}{space 1}   12.39{col 60}{space 3}0.000{col 68}{space 4} .1634581{col 81}{space 3} .2248847
{txt}{space 23}52  {c |}{col 28}{res}{space 2} .1489796{col 40}{space 2} .0138768{col 51}{space 1}   10.74{col 60}{space 3}0.000{col 68}{space 4} .1217815{col 81}{space 3} .1761777
{txt}{space 26} {c |}
{space 22}year {c |}
{space 21}2003  {c |}{col 28}{res}{space 2}-.0076578{col 40}{space 2} .0069649{col 51}{space 1}   -1.10{col 60}{space 3}0.272{col 68}{space 4}-.0213088{col 81}{space 3} .0059931
{txt}{space 21}2004  {c |}{col 28}{res}{space 2} .0018751{col 40}{space 2} .0071863{col 51}{space 1}    0.26{col 60}{space 3}0.794{col 68}{space 4}-.0122098{col 81}{space 3}   .01596
{txt}{space 21}2005  {c |}{col 28}{res}{space 2}-.0039852{col 40}{space 2} .0075092{col 51}{space 1}   -0.53{col 60}{space 3}0.596{col 68}{space 4}-.0187029{col 81}{space 3} .0107324
{txt}{space 21}2006  {c |}{col 28}{res}{space 2} .0096008{col 40}{space 2} .0077429{col 51}{space 1}    1.24{col 60}{space 3}0.215{col 68}{space 4} -.005575{col 81}{space 3} .0247767
{txt}{space 21}2007  {c |}{col 28}{res}{space 2} .0167029{col 40}{space 2}  .008007{col 51}{space 1}    2.09{col 60}{space 3}0.037{col 68}{space 4} .0010094{col 81}{space 3} .0323964
{txt}{space 21}2008  {c |}{col 28}{res}{space 2} .0085843{col 40}{space 2} .0081128{col 51}{space 1}    1.06{col 60}{space 3}0.290{col 68}{space 4}-.0073165{col 81}{space 3}  .024485
{txt}{space 21}2009  {c |}{col 28}{res}{space 2} .0025736{col 40}{space 2} .0099711{col 51}{space 1}    0.26{col 60}{space 3}0.796{col 68}{space 4}-.0169694{col 81}{space 3} .0221166
{txt}{space 21}2010  {c |}{col 28}{res}{space 2} .0372545{col 40}{space 2} .0111941{col 51}{space 1}    3.33{col 60}{space 3}0.001{col 68}{space 4} .0153144{col 81}{space 3} .0591946
{txt}{space 21}2011  {c |}{col 28}{res}{space 2} .0199464{col 40}{space 2} .0099612{col 51}{space 1}    2.00{col 60}{space 3}0.045{col 68}{space 4} .0004228{col 81}{space 3}   .03947
{txt}{space 21}2012  {c |}{col 28}{res}{space 2} .0088633{col 40}{space 2} .0097652{col 51}{space 1}    0.91{col 60}{space 3}0.364{col 68}{space 4}-.0102762{col 81}{space 3} .0280028
{txt}{space 21}2013  {c |}{col 28}{res}{space 2} .0128942{col 40}{space 2} .0104589{col 51}{space 1}    1.23{col 60}{space 3}0.218{col 68}{space 4} -.007605{col 81}{space 3} .0333933
{txt}{space 21}2014  {c |}{col 28}{res}{space 2} .0215128{col 40}{space 2} .0109878{col 51}{space 1}    1.96{col 60}{space 3}0.050{col 68}{space 4}-.0000229{col 81}{space 3} .0430485
{txt}{space 21}2015  {c |}{col 28}{res}{space 2} .0190579{col 40}{space 2} .0113851{col 51}{space 1}    1.67{col 60}{space 3}0.094{col 68}{space 4}-.0032566{col 81}{space 3} .0413724
{txt}{space 21}2016  {c |}{col 28}{res}{space 2}  .026647{col 40}{space 2} .0111054{col 51}{space 1}    2.40{col 60}{space 3}0.016{col 68}{space 4} .0048809{col 81}{space 3} .0484132
{txt}{space 21}2017  {c |}{col 28}{res}{space 2}  .046301{col 40}{space 2} .0117851{col 51}{space 1}    3.93{col 60}{space 3}0.000{col 68}{space 4} .0232028{col 81}{space 3} .0693993
{txt}{space 21}2018  {c |}{col 28}{res}{space 2} .0538291{col 40}{space 2} .0126944{col 51}{space 1}    4.24{col 60}{space 3}0.000{col 68}{space 4} .0289486{col 81}{space 3} .0787097
{txt}{space 21}2019  {c |}{col 28}{res}{space 2} .0426536{col 40}{space 2} .0124616{col 51}{space 1}    3.42{col 60}{space 3}0.001{col 68}{space 4} .0182292{col 81}{space 3}  .067078
{txt}{space 21}2020  {c |}{col 28}{res}{space 2} .0419685{col 40}{space 2} .0163242{col 51}{space 1}    2.57{col 60}{space 3}0.010{col 68}{space 4} .0099737{col 81}{space 3} .0739632
{txt}{space 21}2021  {c |}{col 28}{res}{space 2} .1759757{col 40}{space 2} .0186794{col 51}{space 1}    9.42{col 60}{space 3}0.000{col 68}{space 4} .1393648{col 81}{space 3} .2125867
{txt}{space 21}2022  {c |}{col 28}{res}{space 2} .1534289{col 40}{space 2} .0194995{col 51}{space 1}    7.87{col 60}{space 3}0.000{col 68}{space 4} .1152106{col 81}{space 3} .1916472
{txt}{space 26} {c |}
{space 2}adoptcohort_2002_itadopt {c |}{col 28}{res}{space 2} .1550496{col 40}{space 2} .0282083{col 51}{space 1}    5.50{col 60}{space 3}0.000{col 68}{space 4} .0997623{col 81}{space 3} .2103369
{txt}{space 2}adoptcohort_2004_itadopt {c |}{col 28}{res}{space 2} .0346092{col 40}{space 2} .0210097{col 51}{space 1}    1.65{col 60}{space 3}0.099{col 68}{space 4}-.0065691{col 81}{space 3} .0757875
{txt}{space 2}adoptcohort_2006_itadopt {c |}{col 28}{res}{space 2}  .005424{col 40}{space 2} .0127169{col 51}{space 1}    0.43{col 60}{space 3}0.670{col 68}{space 4}-.0195007{col 81}{space 3} .0303487
{txt}{space 2}adoptcohort_2007_itadopt {c |}{col 28}{res}{space 2} .0196358{col 40}{space 2} .0128969{col 51}{space 1}    1.52{col 60}{space 3}0.128{col 68}{space 4}-.0056416{col 81}{space 3} .0449132
{txt}{space 2}adoptcohort_2009_itadopt {c |}{col 28}{res}{space 2} .1221623{col 40}{space 2} .0119256{col 51}{space 1}   10.24{col 60}{space 3}0.000{col 68}{space 4} .0987885{col 81}{space 3} .1455361
{txt}{space 2}adoptcohort_2010_itadopt {c |}{col 28}{res}{space 2} .0558388{col 40}{space 2} .0147721{col 51}{space 1}    3.78{col 60}{space 3}0.000{col 68}{space 4} .0268861{col 81}{space 3} .0847915
{txt}{space 2}adoptcohort_2013_itadopt {c |}{col 28}{res}{space 2} .0230175{col 40}{space 2} .0097285{col 51}{space 1}    2.37{col 60}{space 3}0.018{col 68}{space 4} .0039499{col 81}{space 3} .0420851
{txt}{space 2}adoptcohort_2014_itadopt {c |}{col 28}{res}{space 2} -.118058{col 40}{space 2} .0277088{col 51}{space 1}   -4.26{col 60}{space 3}0.000{col 68}{space 4}-.1723663{col 81}{space 3}-.0637497
{txt}{space 2}adoptcohort_2015_itadopt {c |}{col 28}{res}{space 2}-.0276981{col 40}{space 2} .0129666{col 51}{space 1}   -2.14{col 60}{space 3}0.033{col 68}{space 4}-.0531122{col 81}{space 3}-.0022839
{txt}{space 2}adoptcohort_2016_itadopt {c |}{col 28}{res}{space 2}-.1032736{col 40}{space 2} .0111926{col 51}{space 1}   -9.23{col 60}{space 3}0.000{col 68}{space 4}-.1252108{col 81}{space 3}-.0813364
{txt}{space 2}adoptcohort_2017_itadopt {c |}{col 28}{res}{space 2}-.0105988{col 40}{space 2} .0129457{col 51}{space 1}   -0.82{col 60}{space 3}0.413{col 68}{space 4}-.0359719{col 81}{space 3} .0147744
{txt}{space 2}adoptcohort_2018_itadopt {c |}{col 28}{res}{space 2}-.0905858{col 40}{space 2} .0144353{col 51}{space 1}   -6.28{col 60}{space 3}0.000{col 68}{space 4}-.1188784{col 81}{space 3}-.0622932
{txt}{space 2}adoptcohort_2020_itadopt {c |}{col 28}{res}{space 2} .0643877{col 40}{space 2} .0238358{col 51}{space 1}    2.70{col 60}{space 3}0.007{col 68}{space 4} .0176704{col 81}{space 3} .1111051
{txt}{space 2}adoptcohort_2021_itadopt {c |}{col 28}{res}{space 2}-.0616838{col 40}{space 2}  .036997{col 51}{space 1}   -1.67{col 60}{space 3}0.095{col 68}{space 4}-.1341965{col 81}{space 3}  .010829
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. *
. 
. ** COMPUTE PSEUDO R^2 [SSE / (SSE + SSR) = EXPLAINED/PREDICTED SUM OF SQUARES / (EXPLAINED/PREDICTED SUM OF SQUARES + RESIDUAL SUM OF SQUARES)] = SSE / SST
. 
. predict predsy_m1 if e(sample)
{txt}(statistic {bf:mean} assumed; mean function)
{res}{txt}
{com}. predict residsy_m1 if e(sample), residuals
{res}{txt}(5,552 missing values generated)

{com}. 
. gen sse_m1 = predsy_m1 * predsy_m1 if e(sample)
{txt}(5,552 missing values generated)

{com}. gen ssr_m1 = residsy_m1 * residsy_m1 if e(sample)
{txt}(5,552 missing values generated)

{com}. 
. egen sum_sse_m1 = total(sse_m1) if e(sample)
{txt}(5,551 missing values generated)

{com}. egen sum_ssr_m1 = total(ssr_m1) if e(sample)
{txt}(5,551 missing values generated)

{com}. 
. gen r2_m1 = sum_ssr_m1/(sum_sse_m1 + sum_ssr_m1)
{txt}(5,551 missing values generated)

{com}. 
. sum r2_m1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}r2_m1 {c |}{res}      7,000    .2215529           0   .2215529   .2215529
{txt}
{com}. 
. 
. 
. * [MODEL 1: TOTAL ERROR  RATE] FIGURE 3A:  UNCONDITIONAL ADAPTATION EFFECTS -- E(Y) [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]
. 
. margins, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:6,999}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .1909289{col 26}{space 2} .0035144{col 37}{space 1}   54.33{col 46}{space 3}0.000{col 54}{space 4} .1840407{col 67}{space 3} .1978171
{txt}{space 10}2  {c |}{col 14}{res}{space 2}  .190145{col 26}{space 2} .0032196{col 37}{space 1}   59.06{col 46}{space 3}0.000{col 54}{space 4} .1838348{col 67}{space 3} .1964552
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .1863092{col 26}{space 2} .0021004{col 37}{space 1}   88.70{col 46}{space 3}0.000{col 54}{space 4} .1821924{col 67}{space 3}  .190426
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .1818781{col 26}{space 2} .0019998{col 37}{space 1}   90.95{col 46}{space 3}0.000{col 54}{space 4} .1779585{col 67}{space 3} .1857977
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .1776165{col 26}{space 2} .0028882{col 37}{space 1}   61.50{col 46}{space 3}0.000{col 54}{space 4} .1719558{col 67}{space 3} .1832773
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .1735054{col 26}{space 2} .0039147{col 37}{space 1}   44.32{col 46}{space 3}0.000{col 54}{space 4} .1658326{col 67}{space 3} .1811781
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .1695255{col 26}{space 2} .0048371{col 37}{space 1}   35.05{col 46}{space 3}0.000{col 54}{space 4}  .160045{col 67}{space 3} .1790059
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .1656578{col 26}{space 2} .0056103{col 37}{space 1}   29.53{col 46}{space 3}0.000{col 54}{space 4} .1546619{col 67}{space 3} .1766537
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .1618831{col 26}{space 2}  .006234{col 37}{space 1}   25.97{col 46}{space 3}0.000{col 54}{space 4} .1496646{col 67}{space 3} .1741015
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .1581824{col 26}{space 2} .0067214{col 37}{space 1}   23.53{col 46}{space 3}0.000{col 54}{space 4} .1450087{col 67}{space 3}  .171356
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .1545364{col 26}{space 2} .0070904{col 37}{space 1}   21.80{col 46}{space 3}0.000{col 54}{space 4} .1406395{col 67}{space 3} .1684333
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .1509262{col 26}{space 2} .0073614{col 37}{space 1}   20.50{col 46}{space 3}0.000{col 54}{space 4} .1364981{col 67}{space 3} .1653544
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) ///
> legend(on order(1 "Unconditional Adaptation") pos(6) ring(2) cols(2) size(9pt))  ///
> title(" {c -(}bf:FIGURE 3A{c )-}""{c -(}bf:Unconditional Adaptation Effect{c )-}" "{c -(}bf:(Total Program Error Rate [MODEL 1]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+2 r-6)) ///
> xtitle("Months since IT Reform", size(10pt) margin(t+2 b+2)) ///
> ytitle("Total Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{res}{txt}
{com}. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model 1.FIGURE 3A.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model 1.FIGURE 3A.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model 1.FIGURE 3A.04-10-2025.gph} saved

{com}. 
. *
. *
. *
. *
. 
. * [MODEL 1: TOTAL PROGRAM ERROR  RATE] FIGURE 3B:  MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (tot_interstate_cat==2) & LOW COMPLEXITY (tot_interstate_cat==0) VALUES [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]: ***
. 
. margins r.tot_interstate_cat if tot_interstate_cat==0|tot_interstate_cat==2, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:3,574}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 23}{c TT}{hline 11}{hline 12}{hline 11}
{col 24}{text}{c |}         df{col 36}        chi2{col 48}     P>chi2
{res}{col 1}{text}{hline 23}{c +}{hline 11}{hline 12}{hline 11}
tot_interstate_cat@_at {c |}
{space 10}(2 vs 0)  1  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     0.02{col 48}{space 2}   0.8996
{txt}{space 10}(2 vs 0)  2  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     0.06{col 48}{space 2}   0.8066
{txt}{space 10}(2 vs 0)  3  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     0.56{col 48}{space 2}   0.4534
{txt}{space 10}(2 vs 0)  4  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     0.98{col 48}{space 2}   0.3210
{txt}{space 10}(2 vs 0)  5  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     0.90{col 48}{space 2}   0.3417
{txt}{space 10}(2 vs 0)  6  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     0.62{col 48}{space 2}   0.4328
{txt}{space 10}(2 vs 0)  7  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     0.31{col 48}{space 2}   0.5804
{txt}{space 10}(2 vs 0)  8  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     0.07{col 48}{space 2}   0.7861
{txt}{space 10}(2 vs 0)  9  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     0.00{col 48}{space 2}   0.9539
{txt}{space 10}(2 vs 0) 10  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     0.19{col 48}{space 2}   0.6633
{txt}{space 10}(2 vs 0) 11  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     0.74{col 48}{space 2}   0.3902
{txt}{space 10}(2 vs 0) 12  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     1.74{col 48}{space 2}   0.1865
{col 1}{text}                Joint {col 24}{c |}{result}  (not testable)
{col 1}{text}{hline 23}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 24}{c |}{col 36} Delta-method
{col 24}{c |}   Contrast{col 36}   std. err.{col 48}     [95% con{col 61}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
tot_interstate_cat@_at {c |}
{space 10}(2 vs 0)  1  {c |}{col 24}{res}{space 2} .0007238{col 36}{space 2} .0057359{col 47}{space 5}-.0105184{col 61}{space 3} .0119659
{txt}{space 10}(2 vs 0)  2  {c |}{col 24}{res}{space 2} .0013613{col 36}{space 2} .0055595{col 47}{space 5}-.0095351{col 61}{space 3} .0122577
{txt}{space 10}(2 vs 0)  3  {c |}{col 24}{res}{space 2} .0039687{col 36}{space 2} .0052935{col 47}{space 5}-.0064065{col 61}{space 3} .0143438
{txt}{space 10}(2 vs 0)  4  {c |}{col 24}{res}{space 2} .0058882{col 36}{space 2} .0059334{col 47}{space 5} -.005741{col 61}{space 3} .0175174
{txt}{space 10}(2 vs 0)  5  {c |}{col 24}{res}{space 2}  .006592{col 36}{space 2} .0069328{col 47}{space 5} -.006996{col 61}{space 3} .0201801
{txt}{space 10}(2 vs 0)  6  {c |}{col 24}{res}{space 2} .0061899{col 36}{space 2} .0078905{col 47}{space 5}-.0092752{col 61}{space 3} .0216549
{txt}{space 10}(2 vs 0)  7  {c |}{col 24}{res}{space 2} .0047913{col 36}{space 2} .0086671{col 47}{space 5}-.0121959{col 61}{space 3} .0217785
{txt}{space 10}(2 vs 0)  8  {c |}{col 24}{res}{space 2}  .002506{col 36}{space 2} .0092352{col 47}{space 5}-.0155945{col 61}{space 3} .0206066
{txt}{space 10}(2 vs 0)  9  {c |}{col 24}{res}{space 2}-.0005563{col 36}{space 2} .0096136{col 47}{space 5}-.0193985{col 61}{space 3} .0182859
{txt}{space 10}(2 vs 0) 10  {c |}{col 24}{res}{space 2} -.004286{col 36}{space 2} .0098434{col 47}{space 5}-.0235786{col 61}{space 3} .0150067
{txt}{space 10}(2 vs 0) 11  {c |}{col 24}{res}{space 2}-.0085734{col 36}{space 2} .0099776{col 47}{space 5}-.0281292{col 61}{space 3} .0109824
{txt}{space 10}(2 vs 0) 12  {c |}{col 24}{res}{space 2}-.0133089{col 36}{space 2} .0100753{col 47}{space 5}-.0330562{col 61}{space 3} .0064384
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
> title(" {c -(}bf:FIGURE 3B{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity{c )-}" "{c -(}bf:(Interstate Claims: Total Program Error Rate [MODEL 1]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+1 r-6)) ///
> xtitle("Months since IT Reform", size(10pt) margin(t+2 b+2)) ///
> ytitle("Total Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model 1.FIGURE 3B.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model 1.FIGURE 3B.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model 1.FIGURE 3B.04-10-2025.gph} saved

{com}. *
. *
. *
. * [MODEL 1: TOTAL PROGRAM ERROR  RATE] FIGURE 3C:  MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (tot_diffoccupseek_cat==2) & LOW COMPLEXITY (tot_extbenefits_cat==0) VALUES [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]: ***
. 
. margins r.tot_diffoccupseek_cat if tot_diffoccupseek_cat==0|tot_diffoccupseek_cat==2, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:3,500}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 26}{c TT}{hline 11}{hline 12}{hline 11}
{col 27}{text}{c |}         df{col 39}        chi2{col 51}     P>chi2
{res}{col 1}{text}{hline 26}{c +}{hline 11}{hline 12}{hline 11}
tot_diffoccupseek_cat@_at {c |}
{space 13}(2 vs 0)  1  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}    37.60{col 51}{space 2}   0.0000
{txt}{space 13}(2 vs 0)  2  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}    38.71{col 51}{space 2}   0.0000
{txt}{space 13}(2 vs 0)  3  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}    32.31{col 51}{space 2}   0.0000
{txt}{space 13}(2 vs 0)  4  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}    17.17{col 51}{space 2}   0.0000
{txt}{space 13}(2 vs 0)  5  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     8.77{col 51}{space 2}   0.0031
{txt}{space 13}(2 vs 0)  6  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     4.72{col 51}{space 2}   0.0298
{txt}{space 13}(2 vs 0)  7  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     2.61{col 51}{space 2}   0.1065
{txt}{space 13}(2 vs 0)  8  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     1.39{col 51}{space 2}   0.2376
{txt}{space 13}(2 vs 0)  9  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     0.67{col 51}{space 2}   0.4140
{txt}{space 13}(2 vs 0) 10  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     0.24{col 51}{space 2}   0.6249
{txt}{space 13}(2 vs 0) 11  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     0.03{col 51}{space 2}   0.8577
{txt}{space 13}(2 vs 0) 12  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     0.01{col 51}{space 2}   0.9046
{col 1}{text}                   Joint {col 27}{c |}{result}  (not testable)
{col 1}{text}{hline 26}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 26}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 27}{c |}{col 39} Delta-method
{col 27}{c |}   Contrast{col 39}   std. err.{col 51}     [95% con{col 64}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
tot_diffoccupseek_cat@_at {c |}
{space 13}(2 vs 0)  1  {c |}{col 27}{res}{space 2} .0299236{col 39}{space 2}   .00488{col 50}{space 5}  .020359{col 64}{space 3} .0394882
{txt}{space 13}(2 vs 0)  2  {c |}{col 27}{res}{space 2} .0294394{col 39}{space 2} .0047315{col 50}{space 5} .0201658{col 64}{space 3}  .038713
{txt}{space 13}(2 vs 0)  3  {c |}{col 27}{res}{space 2}  .026984{col 39}{space 2} .0047473{col 50}{space 5} .0176794{col 64}{space 3} .0362886
{txt}{space 13}(2 vs 0)  4  {c |}{col 27}{res}{space 2} .0239696{col 39}{space 2}  .005785{col 50}{space 5} .0126313{col 64}{space 3} .0353079
{txt}{space 13}(2 vs 0)  5  {c |}{col 27}{res}{space 2} .0208935{col 39}{space 2} .0070551{col 50}{space 5} .0070658{col 64}{space 3} .0347213
{txt}{space 13}(2 vs 0)  6  {c |}{col 27}{res}{space 2} .0177689{col 39}{space 2} .0081767{col 50}{space 5} .0017429{col 64}{space 3} .0337948
{txt}{space 13}(2 vs 0)  7  {c |}{col 27}{res}{space 2} .0146087{col 39}{space 2} .0090509{col 50}{space 5}-.0031308{col 64}{space 3} .0323482
{txt}{space 13}(2 vs 0)  8  {c |}{col 27}{res}{space 2} .0114262{col 39}{space 2} .0096746{col 50}{space 5}-.0075357{col 64}{space 3} .0303881
{txt}{space 13}(2 vs 0)  9  {c |}{col 27}{res}{space 2} .0082344{col 39}{space 2} .0100812{col 50}{space 5}-.0115243{col 64}{space 3} .0279931
{txt}{space 13}(2 vs 0) 10  {c |}{col 27}{res}{space 2} .0050465{col 39}{space 2} .0103218{col 50}{space 5}-.0151838{col 64}{space 3} .0252767
{txt}{space 13}(2 vs 0) 11  {c |}{col 27}{res}{space 2} .0018755{col 39}{space 2}  .010458{col 50}{space 5}-.0186219{col 64}{space 3} .0223728
{txt}{space 13}(2 vs 0) 12  {c |}{col 27}{res}{space 2}-.0012654{col 39}{space 2} .0105571{col 50}{space 5} -.021957{col 64}{space 3} .0194261
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
> title(" {c -(}bf:FIGURE 3C{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity{c )-}" "{c -(}bf:(Seeking Different Occupation: Total Program Error Rate [MODEL 1]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+1 r-6)) ///
> xtitle("Months since IT Reform", size(10pt) margin(t+2 b+2)) ///
> ytitle("Total Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model 1.FIGURE 3C.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model 1.FIGURE 3C.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model 1.FIGURE 3C.04-10-2025.gph} saved

{com}. 
. 
. ****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. *** TESTING H2 & H4: ABSOLUTE TYPE I PROGRAM ERROR RATE ORGANIZATIONAL ADAPTATION  ***
. 
. 
. 
. *** ESTIMATE MODEL 2: TYPE I PROGRAM ERROR RATE [PROPORTION OF TOTAL ERRORS VIA WEEKLY BAM SURVEY AGGREGATED TO MONTHLY OBSERVATIONS: [ONLY STATE, YEAR, AND YEAR-ADOPTION COHORT*TREATMENT UNIT EFFECTS] *** (FIGURES 4A-4C) 
. 
. 
. npregress series t1error_rat  itmod_monthcount  i.t1_interstate_cat    i.t1_diffoccupseek_cat   if itmod_adopt_state==1, asis(demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal  t1_totalnonwhite_rat t1_totalfemale_rat t1_totalageu25o65_rat    i.stateid i.year     adoptcohort_2002_itadopt  adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt  adoptcohort_2020_itadopt  adoptcohort_2021_itadopt)  vce(bootstrap, seed(123) rep(1000))
{res}{txt}(running {bf:npregress} on estimation sample)
{res}
{text}Bootstrap replications ({result:1,000}){text}: 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done
{res}
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{txt}{space 23}29  {c |}{col 28}{res}{space 2} .0207345{col 40}{space 2} .0105306{col 51}{space 1}    1.97{col 60}{space 3}0.049{col 68}{space 4} .0000949{col 81}{space 3} .0413742
{txt}{space 23}31  {c |}{col 28}{res}{space 2}  .023749{col 40}{space 2}  .011917{col 51}{space 1}    1.99{col 60}{space 3}0.046{col 68}{space 4} .0003921{col 81}{space 3} .0471059
{txt}{space 23}33  {c |}{col 28}{res}{space 2}-.0027782{col 40}{space 2} .0070328{col 51}{space 1}   -0.40{col 60}{space 3}0.693{col 68}{space 4}-.0165622{col 81}{space 3} .0110057
{txt}{space 23}35  {c |}{col 28}{res}{space 2}-.0085537{col 40}{space 2} .0134132{col 51}{space 1}   -0.64{col 60}{space 3}0.524{col 68}{space 4}-.0348431{col 81}{space 3} .0177358
{txt}{space 23}38  {c |}{col 28}{res}{space 2} .0794755{col 40}{space 2} .0169749{col 51}{space 1}    4.68{col 60}{space 3}0.000{col 68}{space 4} .0462053{col 81}{space 3} .1127458
{txt}{space 23}40  {c |}{col 28}{res}{space 2}-.0048096{col 40}{space 2}  .006435{col 51}{space 1}   -0.75{col 60}{space 3}0.455{col 68}{space 4}-.0174221{col 81}{space 3} .0078028
{txt}{space 23}42  {c |}{col 28}{res}{space 2} .0267684{col 40}{space 2} .0056776{col 51}{space 1}    4.71{col 60}{space 3}0.000{col 68}{space 4} .0156404{col 81}{space 3} .0378963
{txt}{space 23}44  {c |}{col 28}{res}{space 2}  .023994{col 40}{space 2} .0093757{col 51}{space 1}    2.56{col 60}{space 3}0.010{col 68}{space 4} .0056179{col 81}{space 3} .0423701
{txt}{space 23}46  {c |}{col 28}{res}{space 2} .0252104{col 40}{space 2} .0073473{col 51}{space 1}    3.43{col 60}{space 3}0.001{col 68}{space 4} .0108099{col 81}{space 3} .0396108
{txt}{space 23}47  {c |}{col 28}{res}{space 2}-.0258801{col 40}{space 2} .0092005{col 51}{space 1}   -2.81{col 60}{space 3}0.005{col 68}{space 4}-.0439128{col 81}{space 3}-.0078475
{txt}{space 23}50  {c |}{col 28}{res}{space 2} .0654031{col 40}{space 2} .0141901{col 51}{space 1}    4.61{col 60}{space 3}0.000{col 68}{space 4} .0375911{col 81}{space 3} .0932151
{txt}{space 23}51  {c |}{col 28}{res}{space 2} .0948899{col 40}{space 2}  .010164{col 51}{space 1}    9.34{col 60}{space 3}0.000{col 68}{space 4} .0749687{col 81}{space 3}  .114811
{txt}{space 23}52  {c |}{col 28}{res}{space 2} .0413385{col 40}{space 2} .0088654{col 51}{space 1}    4.66{col 60}{space 3}0.000{col 68}{space 4} .0239626{col 81}{space 3} .0587145
{txt}{space 26} {c |}
{space 22}year {c |}
{space 21}2003  {c |}{col 28}{res}{space 2}-.0060864{col 40}{space 2} .0041096{col 51}{space 1}   -1.48{col 60}{space 3}0.139{col 68}{space 4}-.0141411{col 81}{space 3} .0019682
{txt}{space 21}2004  {c |}{col 28}{res}{space 2}-.0107346{col 40}{space 2} .0041956{col 51}{space 1}   -2.56{col 60}{space 3}0.011{col 68}{space 4}-.0189579{col 81}{space 3}-.0025114
{txt}{space 21}2005  {c |}{col 28}{res}{space 2} -.010027{col 40}{space 2} .0044653{col 51}{space 1}   -2.25{col 60}{space 3}0.025{col 68}{space 4}-.0187788{col 81}{space 3}-.0012752
{txt}{space 21}2006  {c |}{col 28}{res}{space 2}-.0086031{col 40}{space 2} .0046974{col 51}{space 1}   -1.83{col 60}{space 3}0.067{col 68}{space 4}-.0178099{col 81}{space 3} .0006037
{txt}{space 21}2007  {c |}{col 28}{res}{space 2}-.0065952{col 40}{space 2} .0048438{col 51}{space 1}   -1.36{col 60}{space 3}0.173{col 68}{space 4}-.0160889{col 81}{space 3} .0028985
{txt}{space 21}2008  {c |}{col 28}{res}{space 2}-.0087883{col 40}{space 2} .0053856{col 51}{space 1}   -1.63{col 60}{space 3}0.103{col 68}{space 4}-.0193439{col 81}{space 3} .0017673
{txt}{space 21}2009  {c |}{col 28}{res}{space 2}-.0011419{col 40}{space 2} .0064881{col 51}{space 1}   -0.18{col 60}{space 3}0.860{col 68}{space 4}-.0138583{col 81}{space 3} .0115745
{txt}{space 21}2010  {c |}{col 28}{res}{space 2} .0150186{col 40}{space 2} .0070313{col 51}{space 1}    2.14{col 60}{space 3}0.033{col 68}{space 4} .0012376{col 81}{space 3} .0287997
{txt}{space 21}2011  {c |}{col 28}{res}{space 2}-.0020875{col 40}{space 2} .0063676{col 51}{space 1}   -0.33{col 60}{space 3}0.743{col 68}{space 4}-.0145678{col 81}{space 3} .0103928
{txt}{space 21}2012  {c |}{col 28}{res}{space 2}-.0012734{col 40}{space 2} .0066128{col 51}{space 1}   -0.19{col 60}{space 3}0.847{col 68}{space 4}-.0142343{col 81}{space 3} .0116875
{txt}{space 21}2013  {c |}{col 28}{res}{space 2} -.015408{col 40}{space 2} .0065597{col 51}{space 1}   -2.35{col 60}{space 3}0.019{col 68}{space 4}-.0282647{col 81}{space 3}-.0025513
{txt}{space 21}2014  {c |}{col 28}{res}{space 2}-.0067522{col 40}{space 2} .0074878{col 51}{space 1}   -0.90{col 60}{space 3}0.367{col 68}{space 4} -.021428{col 81}{space 3} .0079236
{txt}{space 21}2015  {c |}{col 28}{res}{space 2}  .000797{col 40}{space 2} .0079525{col 51}{space 1}    0.10{col 60}{space 3}0.920{col 68}{space 4}-.0147897{col 81}{space 3} .0163837
{txt}{space 21}2016  {c |}{col 28}{res}{space 2} .0057567{col 40}{space 2}  .007826{col 51}{space 1}    0.74{col 60}{space 3}0.462{col 68}{space 4}-.0095819{col 81}{space 3} .0210954
{txt}{space 21}2017  {c |}{col 28}{res}{space 2} .0164691{col 40}{space 2}  .007902{col 51}{space 1}    2.08{col 60}{space 3}0.037{col 68}{space 4} .0009814{col 81}{space 3} .0319567
{txt}{space 21}2018  {c |}{col 28}{res}{space 2} .0196524{col 40}{space 2}   .00937{col 51}{space 1}    2.10{col 60}{space 3}0.036{col 68}{space 4} .0012875{col 81}{space 3} .0380174
{txt}{space 21}2019  {c |}{col 28}{res}{space 2} .0153999{col 40}{space 2} .0089367{col 51}{space 1}    1.72{col 60}{space 3}0.085{col 68}{space 4}-.0021157{col 81}{space 3} .0329155
{txt}{space 21}2020  {c |}{col 28}{res}{space 2} .0613292{col 40}{space 2} .0105384{col 51}{space 1}    5.82{col 60}{space 3}0.000{col 68}{space 4} .0406742{col 81}{space 3} .0819841
{txt}{space 21}2021  {c |}{col 28}{res}{space 2} .1182361{col 40}{space 2} .0118249{col 51}{space 1}   10.00{col 60}{space 3}0.000{col 68}{space 4} .0950597{col 81}{space 3} .1414124
{txt}{space 21}2022  {c |}{col 28}{res}{space 2} .0872552{col 40}{space 2} .0126984{col 51}{space 1}    6.87{col 60}{space 3}0.000{col 68}{space 4} .0623669{col 81}{space 3} .1121436
{txt}{space 26} {c |}
{space 2}adoptcohort_2002_itadopt {c |}{col 28}{res}{space 2} .0870324{col 40}{space 2} .0116766{col 51}{space 1}    7.45{col 60}{space 3}0.000{col 68}{space 4} .0641468{col 81}{space 3} .1099181
{txt}{space 2}adoptcohort_2004_itadopt {c |}{col 28}{res}{space 2}  .053737{col 40}{space 2} .0117039{col 51}{space 1}    4.59{col 60}{space 3}0.000{col 68}{space 4} .0307977{col 81}{space 3} .0766763
{txt}{space 2}adoptcohort_2006_itadopt {c |}{col 28}{res}{space 2} .0085861{col 40}{space 2}  .008013{col 51}{space 1}    1.07{col 60}{space 3}0.284{col 68}{space 4}-.0071191{col 81}{space 3} .0242913
{txt}{space 2}adoptcohort_2007_itadopt {c |}{col 28}{res}{space 2} .0243766{col 40}{space 2} .0075495{col 51}{space 1}    3.23{col 60}{space 3}0.001{col 68}{space 4} .0095799{col 81}{space 3} .0391732
{txt}{space 2}adoptcohort_2009_itadopt {c |}{col 28}{res}{space 2} .0132694{col 40}{space 2} .0060456{col 51}{space 1}    2.19{col 60}{space 3}0.028{col 68}{space 4} .0014203{col 81}{space 3} .0251185
{txt}{space 2}adoptcohort_2010_itadopt {c |}{col 28}{res}{space 2} .0015167{col 40}{space 2}   .00721{col 51}{space 1}    0.21{col 60}{space 3}0.833{col 68}{space 4}-.0126147{col 81}{space 3} .0156482
{txt}{space 2}adoptcohort_2013_itadopt {c |}{col 28}{res}{space 2} .0145131{col 40}{space 2} .0057133{col 51}{space 1}    2.54{col 60}{space 3}0.011{col 68}{space 4} .0033152{col 81}{space 3}  .025711
{txt}{space 2}adoptcohort_2014_itadopt {c |}{col 28}{res}{space 2}-.0837937{col 40}{space 2} .0182197{col 51}{space 1}   -4.60{col 60}{space 3}0.000{col 68}{space 4}-.1195036{col 81}{space 3}-.0480837
{txt}{space 2}adoptcohort_2015_itadopt {c |}{col 28}{res}{space 2}-.0332665{col 40}{space 2}  .007952{col 51}{space 1}   -4.18{col 60}{space 3}0.000{col 68}{space 4} -.048852{col 81}{space 3}-.0176809
{txt}{space 2}adoptcohort_2016_itadopt {c |}{col 28}{res}{space 2}-.0299982{col 40}{space 2} .0074727{col 51}{space 1}   -4.01{col 60}{space 3}0.000{col 68}{space 4}-.0446444{col 81}{space 3} -.015352
{txt}{space 2}adoptcohort_2017_itadopt {c |}{col 28}{res}{space 2}-.0468857{col 40}{space 2}  .007155{col 51}{space 1}   -6.55{col 60}{space 3}0.000{col 68}{space 4}-.0609093{col 81}{space 3}-.0328621
{txt}{space 2}adoptcohort_2018_itadopt {c |}{col 28}{res}{space 2}-.0739224{col 40}{space 2} .0093237{col 51}{space 1}   -7.93{col 60}{space 3}0.000{col 68}{space 4}-.0921965{col 81}{space 3}-.0556483
{txt}{space 2}adoptcohort_2020_itadopt {c |}{col 28}{res}{space 2} .0332934{col 40}{space 2} .0186852{col 51}{space 1}    1.78{col 60}{space 3}0.075{col 68}{space 4}-.0033289{col 81}{space 3} .0699158
{txt}{space 2}adoptcohort_2021_itadopt {c |}{col 28}{res}{space 2}-.1010319{col 40}{space 2} .0291875{col 51}{space 1}   -3.46{col 60}{space 3}0.001{col 68}{space 4}-.1582383{col 81}{space 3}-.0438256
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. *
. 
. ** COMPUTE PSEUDO R^2 [SSE / (SSE + SSR) = EXPLAINED/PREDICTED SUM OF SQUARES / (EXPLAINED/PREDICTED SUM OF SQUARES + RESIDUAL SUM OF SQUARES)] = SSE / SST
. 
. predict predsy_m2 if e(sample)
{txt}(statistic {bf:mean} assumed; mean function)
{res}{txt}
{com}. predict residsy_m2 if e(sample), residuals
{res}{txt}(5,289 missing values generated)

{com}. 
. gen sse_m2 = predsy_m2 * predsy_m2 if e(sample)
{txt}(5,289 missing values generated)

{com}. gen ssr_m2 = residsy_m2 * residsy_m2 if e(sample)
{txt}(5,289 missing values generated)

{com}. 
. egen sum_sse_m2 = total(sse_m2) if e(sample)
{txt}(5,289 missing values generated)

{com}. egen sum_ssr_m2 = total(ssr_m2) if e(sample)
{txt}(5,289 missing values generated)

{com}. 
. gen r2_m2 = sum_ssr_m2/(sum_sse_m2 + sum_ssr_m2)
{txt}(5,289 missing values generated)

{com}. 
. sum r2_m2

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}r2_m2 {c |}{res}      7,262    .4520501           0   .4520501   .4520501
{txt}
{com}. 
. 
. 
. * [MODEL 2: TYPE I PROGRAM ERROR RATE] FIGURE 4A: UNCONDITIONAL ADAPTATION EFFECTS -- E(Y) [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]
. 
. margins, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:7,262}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0741946{col 26}{space 2} .0024411{col 37}{space 1}   30.39{col 46}{space 3}0.000{col 54}{space 4} .0694101{col 67}{space 3} .0789792
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .0730234{col 26}{space 2} .0022945{col 37}{space 1}   31.82{col 46}{space 3}0.000{col 54}{space 4} .0685262{col 67}{space 3} .0775206
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0674782{col 26}{space 2} .0017089{col 37}{space 1}   39.49{col 46}{space 3}0.000{col 54}{space 4} .0641287{col 67}{space 3} .0708277
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .0614754{col 26}{space 2} .0014376{col 37}{space 1}   42.76{col 46}{space 3}0.000{col 54}{space 4} .0586577{col 67}{space 3} .0642931
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .0561328{col 26}{space 2} .0016365{col 37}{space 1}   34.30{col 46}{space 3}0.000{col 54}{space 4} .0529253{col 67}{space 3} .0593402
{txt}{space 10}6  {c |}{col 14}{res}{space 2}  .051397{col 26}{space 2} .0020491{col 37}{space 1}   25.08{col 46}{space 3}0.000{col 54}{space 4} .0473808{col 67}{space 3} .0554131
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .0472146{col 26}{space 2} .0024928{col 37}{space 1}   18.94{col 46}{space 3}0.000{col 54}{space 4} .0423288{col 67}{space 3} .0521005
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .0435324{col 26}{space 2} .0029032{col 37}{space 1}   14.99{col 46}{space 3}0.000{col 54}{space 4} .0378422{col 67}{space 3} .0492226
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .0402968{col 26}{space 2} .0032628{col 37}{space 1}   12.35{col 46}{space 3}0.000{col 54}{space 4} .0339019{col 67}{space 3} .0466917
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .0374546{col 26}{space 2} .0035707{col 37}{space 1}   10.49{col 46}{space 3}0.000{col 54}{space 4} .0304561{col 67}{space 3}  .044453
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .0349523{col 26}{space 2} .0038327{col 37}{space 1}    9.12{col 46}{space 3}0.000{col 54}{space 4} .0274404{col 67}{space 3} .0424642
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .0327366{col 26}{space 2} .0040573{col 37}{space 1}    8.07{col 46}{space 3}0.000{col 54}{space 4} .0247844{col 67}{space 3} .0406888
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) ///
> legend(on order(1 "Unconditional Adaptation") pos(6) ring(2) cols(2) size(9pt))  ///
> title(" {c -(}bf:FIGURE 4A{c )-}""{c -(}bf:Unconditional Adaptation Effect{c )-}" "{c -(}bf:(Absolute Type I Program Error Rate [MODEL 2]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+2 r-6)) ///
> xtitle("Months since IT Reform", size(10pt) margin(t+2 b+2)) ///
> ytitle("Absolute Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{res}{txt}
{com}. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model 2.FIGURE 4A.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model 2.FIGURE 4A.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model 2.FIGURE 4A.04-10-2025.gph} saved

{com}. 
. 
. *
. *
. *
. *
. 
. * [MODEL 2: ABSOLUTE TYPE I PROGRAM ERROR RATE] FIGURE 4B:  MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (t1_interstate_cat==2) & LOW COMPLEXITY (t1_interstate_cat==0) VALUES [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]: ***
. 
. margins r.t1_interstate_cat if t1_interstate_cat==0|t1_interstate_cat==2, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:3,767}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 22}{c TT}{hline 11}{hline 12}{hline 11}
{col 23}{text}{c |}         df{col 35}        chi2{col 47}     P>chi2
{res}{col 1}{text}{hline 22}{c +}{hline 11}{hline 12}{hline 11}
t1_interstate_cat@_at {c |}
{space 9}(2 vs 0)  1  {res}{col 23}{text}{c |}{result}{space 2}        1{col 35}{space 3}     0.08{col 47}{space 2}   0.7785
{txt}{space 9}(2 vs 0)  2  {res}{col 23}{text}{c |}{result}{space 2}        1{col 35}{space 3}     0.10{col 47}{space 2}   0.7540
{txt}{space 9}(2 vs 0)  3  {res}{col 23}{text}{c |}{result}{space 2}        1{col 35}{space 3}     0.19{col 47}{space 2}   0.6662
{txt}{space 9}(2 vs 0)  4  {res}{col 23}{text}{c |}{result}{space 2}        1{col 35}{space 3}     0.19{col 47}{space 2}   0.6623
{txt}{space 9}(2 vs 0)  5  {res}{col 23}{text}{c |}{result}{space 2}        1{col 35}{space 3}     0.11{col 47}{space 2}   0.7407
{txt}{space 9}(2 vs 0)  6  {res}{col 23}{text}{c |}{result}{space 2}        1{col 35}{space 3}     0.03{col 47}{space 2}   0.8566
{txt}{space 9}(2 vs 0)  7  {res}{col 23}{text}{c |}{result}{space 2}        1{col 35}{space 3}     0.00{col 47}{space 2}   0.9895
{txt}{space 9}(2 vs 0)  8  {res}{col 23}{text}{c |}{result}{space 2}        1{col 35}{space 3}     0.03{col 47}{space 2}   0.8674
{txt}{space 9}(2 vs 0)  9  {res}{col 23}{text}{c |}{result}{space 2}        1{col 35}{space 3}     0.13{col 47}{space 2}   0.7187
{txt}{space 9}(2 vs 0) 10  {res}{col 23}{text}{c |}{result}{space 2}        1{col 35}{space 3}     0.32{col 47}{space 2}   0.5700
{txt}{space 9}(2 vs 0) 11  {res}{col 23}{text}{c |}{result}{space 2}        1{col 35}{space 3}     0.62{col 47}{space 2}   0.4296
{txt}{space 9}(2 vs 0) 12  {res}{col 23}{text}{c |}{result}{space 2}        1{col 35}{space 3}     1.05{col 47}{space 2}   0.3064
{col 1}{text}               Joint {col 23}{c |}{result}  (not testable)
{col 1}{text}{hline 22}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 23}{c |}{col 35} Delta-method
{col 23}{c |}   Contrast{col 35}   std. err.{col 47}     [95% con{col 60}f. interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
t1_interstate_cat@_at {c |}
{space 9}(2 vs 0)  1  {c |}{col 23}{res}{space 2} .0010263{col 35}{space 2} .0036486{col 46}{space 5}-.0061248{col 60}{space 3} .0081774
{txt}{space 9}(2 vs 0)  2  {c |}{col 23}{res}{space 2} .0011036{col 35}{space 2} .0035223{col 46}{space 5}-.0058001{col 60}{space 3} .0080072
{txt}{space 9}(2 vs 0)  3  {c |}{col 23}{res}{space 2} .0013612{col 35}{space 2} .0031556{col 46}{space 5}-.0048237{col 60}{space 3} .0075461
{txt}{space 9}(2 vs 0)  4  {c |}{col 23}{res}{space 2} .0014044{col 35}{space 2} .0032153{col 46}{space 5}-.0048975{col 60}{space 3} .0077063
{txt}{space 9}(2 vs 0)  5  {c |}{col 23}{res}{space 2} .0011833{col 35}{space 2} .0035756{col 46}{space 5}-.0058248{col 60}{space 3} .0081914
{txt}{space 9}(2 vs 0)  6  {c |}{col 23}{res}{space 2} .0007253{col 35}{space 2} .0040131{col 46}{space 5}-.0071402{col 60}{space 3} .0085907
{txt}{space 9}(2 vs 0)  7  {c |}{col 23}{res}{space 2} .0000579{col 35}{space 2} .0044155{col 46}{space 5}-.0085964{col 60}{space 3} .0087122
{txt}{space 9}(2 vs 0)  8  {c |}{col 23}{res}{space 2}-.0007914{col 35}{space 2} .0047417{col 46}{space 5} -.010085{col 60}{space 3} .0085021
{txt}{space 9}(2 vs 0)  9  {c |}{col 23}{res}{space 2}-.0017953{col 35}{space 2} .0049841{col 46}{space 5} -.011564{col 60}{space 3} .0079734
{txt}{space 9}(2 vs 0) 10  {c |}{col 23}{res}{space 2}-.0029262{col 35}{space 2} .0051517{col 46}{space 5}-.0130233{col 60}{space 3}  .007171
{txt}{space 9}(2 vs 0) 11  {c |}{col 23}{res}{space 2}-.0041567{col 35}{space 2}  .005262{col 46}{space 5}-.0144701{col 60}{space 3} .0061567
{txt}{space 9}(2 vs 0) 12  {c |}{col 23}{res}{space 2}-.0054594{col 35}{space 2} .0053374{col 46}{space 5}-.0159205{col 60}{space 3} .0050017
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
> title(" {c -(}bf:FIGURE 4B{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity{c )-}" "{c -(}bf:(Interstate Claims: Absolute Type I Program Error Rate [MODEL 2]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+1 r-6)) ///
> xtitle("Months since Adoption", size(10pt) margin(t+2 b+2)) ///
> ytitle("Absolute Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(#3, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model 2.FIGURE 4B.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model 2.FIGURE 4B.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model 2.FIGURE 4B.04-10-2025.gph} saved

{com}. *
. *
. *
. *
. *
. * [MODEL 2: ABSOLUTE TYPE I PROGRAM ERROR RATE] FIGURE 4C:  MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (t1_diffoccupseek_cat==2) & LOW COMPLEXITY (t1_extbenefits_cat==0) VALUES [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]: ***
. 
. margins r.t1_diffoccupseek_cat if t1_diffoccupseek_cat==0|t1_diffoccupseek_cat==2, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:3,688}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 12}{hline 11}
{col 26}{text}{c |}         df{col 38}        chi2{col 50}     P>chi2
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 12}{hline 11}
t1_diffoccupseek_cat@_at {c |}
{space 12}(2 vs 0)  1  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}    11.55{col 50}{space 2}   0.0007
{txt}{space 12}(2 vs 0)  2  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}    12.20{col 50}{space 2}   0.0005
{txt}{space 12}(2 vs 0)  3  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}    13.70{col 50}{space 2}   0.0002
{txt}{space 12}(2 vs 0)  4  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}    11.67{col 50}{space 2}   0.0006
{txt}{space 12}(2 vs 0)  5  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     8.78{col 50}{space 2}   0.0030
{txt}{space 12}(2 vs 0)  6  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     6.70{col 50}{space 2}   0.0096
{txt}{space 12}(2 vs 0)  7  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     5.39{col 50}{space 2}   0.0203
{txt}{space 12}(2 vs 0)  8  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     4.57{col 50}{space 2}   0.0326
{txt}{space 12}(2 vs 0)  9  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     4.05{col 50}{space 2}   0.0443
{txt}{space 12}(2 vs 0) 10  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     3.71{col 50}{space 2}   0.0542
{txt}{space 12}(2 vs 0) 11  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     3.47{col 50}{space 2}   0.0623
{txt}{space 12}(2 vs 0) 12  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     3.30{col 50}{space 2}   0.0692
{col 1}{text}                  Joint {col 26}{c |}{result}  (not testable)
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 26}{c |}{col 38} Delta-method
{col 26}{c |}   Contrast{col 38}   std. err.{col 50}     [95% con{col 63}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
t1_diffoccupseek_cat@_at {c |}
{space 12}(2 vs 0)  1  {c |}{col 26}{res}{space 2} .0115189{col 38}{space 2} .0033899{col 49}{space 5} .0048747{col 63}{space 3}  .018163
{txt}{space 12}(2 vs 0)  2  {c |}{col 26}{res}{space 2} .0114866{col 38}{space 2}  .003289{col 49}{space 5} .0050404{col 63}{space 3} .0179329
{txt}{space 12}(2 vs 0)  3  {c |}{col 26}{res}{space 2} .0113283{col 38}{space 2}  .003061{col 49}{space 5} .0053289{col 63}{space 3} .0173278
{txt}{space 12}(2 vs 0)  4  {c |}{col 26}{res}{space 2} .0111454{col 38}{space 2} .0032628{col 49}{space 5} .0047505{col 63}{space 3} .0175403
{txt}{space 12}(2 vs 0)  5  {c |}{col 26}{res}{space 2} .0109704{col 38}{space 2} .0037022{col 49}{space 5} .0037143{col 63}{space 3} .0182265
{txt}{space 12}(2 vs 0)  6  {c |}{col 26}{res}{space 2} .0108036{col 38}{space 2}  .004173{col 49}{space 5} .0026248{col 63}{space 3} .0189825
{txt}{space 12}(2 vs 0)  7  {c |}{col 26}{res}{space 2} .0106455{col 38}{space 2} .0045853{col 49}{space 5} .0016585{col 63}{space 3} .0196325
{txt}{space 12}(2 vs 0)  8  {c |}{col 26}{res}{space 2} .0104962{col 38}{space 2} .0049107{col 49}{space 5} .0008715{col 63}{space 3}  .020121
{txt}{space 12}(2 vs 0)  9  {c |}{col 26}{res}{space 2} .0103562{col 38}{space 2} .0051487{col 49}{space 5}  .000265{col 63}{space 3} .0204475
{txt}{space 12}(2 vs 0) 10  {c |}{col 26}{res}{space 2} .0102258{col 38}{space 2} .0053123{col 49}{space 5}-.0001861{col 63}{space 3} .0206377
{txt}{space 12}(2 vs 0) 11  {c |}{col 26}{res}{space 2} .0101053{col 38}{space 2} .0054217{col 49}{space 5} -.000521{col 63}{space 3} .0207316
{txt}{space 12}(2 vs 0) 12  {c |}{col 26}{res}{space 2}  .009995{col 38}{space 2} .0055009{col 49}{space 5}-.0007866{col 63}{space 3} .0207766
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
> title(" {c -(}bf:FIGURE 4C{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity{c )-}" "{c -(}bf:(Seeking Different Occupation: Absolute Type I Program Error Rate [MODEL 2]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+1 r-6)) ///
> xtitle("Months since IT Reform", size(10pt) margin(t+2 b+2)) ///
> ytitle("Absolute Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(#3, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model 2.FIGURE 4C.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model 2.FIGURE 4C.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model 2.FIGURE 4C.04-10-2025.gph} saved

{com}. 
. 
. ****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. *** TESTING H2 & H4: RELATIVE TYPE I PROGRAM ERROR RATE [TYPE I ERROR RATE / (TYPE I ERROR RATE + TYPE II ERROR RATE)] ORGANIZATIONAL ADAPTATION ***
. 
. 
. 
. 
. *** ESTIMATE MODEL 3: RELATIVE TYPE I PROGRAM ERROR RATE [WITH ADDITIONAL COVARIATES: PROPORTION OF SAMPLE-WEIGHTED CASES OF TOTAL ERRORS VIA WEEKLY BAM SURVEY AGGREGATED TO MONTHLY OBSERVATIONS: [ONLY STATE, YEAR, AND YEAR-ADOPTION COHORT*TREATMENT UNIT EFFECTS] ***  (FIGURES 4D-4F) 
. 
. 
. npregress series relt1error_rat   itmod_monthcount  i.relt1_interstate_cat   i.relt1_diffoccupseek_cat    if itmod_adopt_state==1, asis(demgovparty repgovparty ln_workload automationrate ln_uiadmin_budget_real benefitgenerosity2 unemp_rate ln_function_sup_avgsalreal tot_totalnonwhite_rat tot_totalfemale_rat tot_totalageu25o65_rat  i.stateid i.year  adoptcohort_2002_itadopt  adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt  adoptcohort_2020_itadopt  adoptcohort_2021_itadopt) vce(bootstrap, seed(123) rep(1000))
{res}{txt}(running {bf:npregress} on estimation sample)
{res}
{text}Bootstrap replications ({result:1,000}){text}: 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{txt}{space 23}29  {c |}{col 28}{res}{space 2}-.1989318{col 40}{space 2} .0418152{col 51}{space 1}   -4.76{col 60}{space 3}0.000{col 68}{space 4}-.2808881{col 81}{space 3}-.1169755
{txt}{space 23}31  {c |}{col 28}{res}{space 2}-.2094408{col 40}{space 2}  .102901{col 51}{space 1}   -2.04{col 60}{space 3}0.042{col 68}{space 4}-.4111231{col 81}{space 3}-.0077584
{txt}{space 23}33  {c |}{col 28}{res}{space 2}-.0594831{col 40}{space 2} .0331111{col 51}{space 1}   -1.80{col 60}{space 3}0.072{col 68}{space 4}-.1243797{col 81}{space 3} .0054135
{txt}{space 23}35  {c |}{col 28}{res}{space 2}-.2223161{col 40}{space 2} .0509788{col 51}{space 1}   -4.36{col 60}{space 3}0.000{col 68}{space 4}-.3222328{col 81}{space 3}-.1223995
{txt}{space 23}38  {c |}{col 28}{res}{space 2}-.0069569{col 40}{space 2} .0503615{col 51}{space 1}   -0.14{col 60}{space 3}0.890{col 68}{space 4}-.1056636{col 81}{space 3} .0917498
{txt}{space 23}40  {c |}{col 28}{res}{space 2}-.0833714{col 40}{space 2} .0315496{col 51}{space 1}   -2.64{col 60}{space 3}0.008{col 68}{space 4}-.1452075{col 81}{space 3}-.0215352
{txt}{space 23}42  {c |}{col 28}{res}{space 2}-.1690276{col 40}{space 2} .0284223{col 51}{space 1}   -5.95{col 60}{space 3}0.000{col 68}{space 4}-.2247343{col 81}{space 3} -.113321
{txt}{space 23}44  {c |}{col 28}{res}{space 2}-.2013123{col 40}{space 2} .0474305{col 51}{space 1}   -4.24{col 60}{space 3}0.000{col 68}{space 4}-.2942744{col 81}{space 3}-.1083502
{txt}{space 23}46  {c |}{col 28}{res}{space 2} -.014922{col 40}{space 2} .0315073{col 51}{space 1}   -0.47{col 60}{space 3}0.636{col 68}{space 4}-.0766752{col 81}{space 3} .0468313
{txt}{space 23}47  {c |}{col 28}{res}{space 2} -.198728{col 40}{space 2} .0386838{col 51}{space 1}   -5.14{col 60}{space 3}0.000{col 68}{space 4}-.2745469{col 81}{space 3}-.1229091
{txt}{space 23}50  {c |}{col 28}{res}{space 2} -.045525{col 40}{space 2} .0494187{col 51}{space 1}   -0.92{col 60}{space 3}0.357{col 68}{space 4}-.1423839{col 81}{space 3} .0513338
{txt}{space 23}51  {c |}{col 28}{res}{space 2} .0066345{col 40}{space 2} .0375699{col 51}{space 1}    0.18{col 60}{space 3}0.860{col 68}{space 4}-.0670012{col 81}{space 3} .0802703
{txt}{space 23}52  {c |}{col 28}{res}{space 2} -.112628{col 40}{space 2} .0338448{col 51}{space 1}   -3.33{col 60}{space 3}0.001{col 68}{space 4}-.1789625{col 81}{space 3}-.0462934
{txt}{space 26} {c |}
{space 22}year {c |}
{space 21}2003  {c |}{col 28}{res}{space 2} .0033206{col 40}{space 2} .0211561{col 51}{space 1}    0.16{col 60}{space 3}0.875{col 68}{space 4}-.0381447{col 81}{space 3} .0447858
{txt}{space 21}2004  {c |}{col 28}{res}{space 2}-.0452115{col 40}{space 2}  .020717{col 51}{space 1}   -2.18{col 60}{space 3}0.029{col 68}{space 4} -.085816{col 81}{space 3}-.0046069
{txt}{space 21}2005  {c |}{col 28}{res}{space 2}-.0297578{col 40}{space 2} .0227629{col 51}{space 1}   -1.31{col 60}{space 3}0.191{col 68}{space 4}-.0743723{col 81}{space 3} .0148567
{txt}{space 21}2006  {c |}{col 28}{res}{space 2}-.0481365{col 40}{space 2} .0222817{col 51}{space 1}   -2.16{col 60}{space 3}0.031{col 68}{space 4}-.0918077{col 81}{space 3}-.0044652
{txt}{space 21}2007  {c |}{col 28}{res}{space 2}-.0599629{col 40}{space 2} .0226119{col 51}{space 1}   -2.65{col 60}{space 3}0.008{col 68}{space 4}-.1042815{col 81}{space 3}-.0156443
{txt}{space 21}2008  {c |}{col 28}{res}{space 2}-.0647798{col 40}{space 2} .0231935{col 51}{space 1}   -2.79{col 60}{space 3}0.005{col 68}{space 4}-.1102381{col 81}{space 3}-.0193214
{txt}{space 21}2009  {c |}{col 28}{res}{space 2}-.0096216{col 40}{space 2} .0263112{col 51}{space 1}   -0.37{col 60}{space 3}0.715{col 68}{space 4}-.0611906{col 81}{space 3} .0419474
{txt}{space 21}2010  {c |}{col 28}{res}{space 2} .0060305{col 40}{space 2} .0264705{col 51}{space 1}    0.23{col 60}{space 3}0.820{col 68}{space 4}-.0458506{col 81}{space 3} .0579117
{txt}{space 21}2011  {c |}{col 28}{res}{space 2}-.0231496{col 40}{space 2} .0258404{col 51}{space 1}   -0.90{col 60}{space 3}0.370{col 68}{space 4}-.0737958{col 81}{space 3} .0274966
{txt}{space 21}2012  {c |}{col 28}{res}{space 2} .0013243{col 40}{space 2} .0257201{col 51}{space 1}    0.05{col 60}{space 3}0.959{col 68}{space 4}-.0490862{col 81}{space 3} .0517348
{txt}{space 21}2013  {c |}{col 28}{res}{space 2}-.0565777{col 40}{space 2} .0264646{col 51}{space 1}   -2.14{col 60}{space 3}0.033{col 68}{space 4}-.1084474{col 81}{space 3} -.004708
{txt}{space 21}2014  {c |}{col 28}{res}{space 2}-.0485744{col 40}{space 2} .0267456{col 51}{space 1}   -1.82{col 60}{space 3}0.069{col 68}{space 4}-.1009947{col 81}{space 3}  .003846
{txt}{space 21}2015  {c |}{col 28}{res}{space 2}-.0215887{col 40}{space 2} .0276227{col 51}{space 1}   -0.78{col 60}{space 3}0.434{col 68}{space 4}-.0757282{col 81}{space 3} .0325507
{txt}{space 21}2016  {c |}{col 28}{res}{space 2}-.0282807{col 40}{space 2}  .027299{col 51}{space 1}   -1.04{col 60}{space 3}0.300{col 68}{space 4}-.0817858{col 81}{space 3} .0252244
{txt}{space 21}2017  {c |}{col 28}{res}{space 2}-.0188147{col 40}{space 2} .0282425{col 51}{space 1}   -0.67{col 60}{space 3}0.505{col 68}{space 4} -.074169{col 81}{space 3} .0365397
{txt}{space 21}2018  {c |}{col 28}{res}{space 2}-.0246277{col 40}{space 2} .0289093{col 51}{space 1}   -0.85{col 60}{space 3}0.394{col 68}{space 4}-.0812889{col 81}{space 3} .0320335
{txt}{space 21}2019  {c |}{col 28}{res}{space 2}-.0607786{col 40}{space 2} .0298373{col 51}{space 1}   -2.04{col 60}{space 3}0.042{col 68}{space 4}-.1192586{col 81}{space 3}-.0022987
{txt}{space 21}2020  {c |}{col 28}{res}{space 2} .0726534{col 40}{space 2}  .036714{col 51}{space 1}    1.98{col 60}{space 3}0.048{col 68}{space 4} .0006953{col 81}{space 3} .1446115
{txt}{space 21}2021  {c |}{col 28}{res}{space 2} .2243566{col 40}{space 2} .0353871{col 51}{space 1}    6.34{col 60}{space 3}0.000{col 68}{space 4} .1549991{col 81}{space 3} .2937141
{txt}{space 21}2022  {c |}{col 28}{res}{space 2} .1086487{col 40}{space 2} .0397235{col 51}{space 1}    2.74{col 60}{space 3}0.006{col 68}{space 4} .0307921{col 81}{space 3} .1865053
{txt}{space 26} {c |}
{space 2}adoptcohort_2002_itadopt {c |}{col 28}{res}{space 2} .2151299{col 40}{space 2} .1497186{col 51}{space 1}    1.44{col 60}{space 3}0.151{col 68}{space 4}-.0783131{col 81}{space 3} .5085729
{txt}{space 2}adoptcohort_2004_itadopt {c |}{col 28}{res}{space 2} .2387945{col 40}{space 2}  .116307{col 51}{space 1}    2.05{col 60}{space 3}0.040{col 68}{space 4}  .010837{col 81}{space 3} .4667519
{txt}{space 2}adoptcohort_2006_itadopt {c |}{col 28}{res}{space 2} .1976305{col 40}{space 2} .1196458{col 51}{space 1}    1.65{col 60}{space 3}0.099{col 68}{space 4}-.0368711{col 81}{space 3}  .432132
{txt}{space 2}adoptcohort_2007_itadopt {c |}{col 28}{res}{space 2} .0440924{col 40}{space 2} .1105858{col 51}{space 1}    0.40{col 60}{space 3}0.690{col 68}{space 4}-.1726518{col 81}{space 3} .2608366
{txt}{space 2}adoptcohort_2009_itadopt {c |}{col 28}{res}{space 2}-.0688823{col 40}{space 2} .1115511{col 51}{space 1}   -0.62{col 60}{space 3}0.537{col 68}{space 4}-.2875183{col 81}{space 3} .1497538
{txt}{space 2}adoptcohort_2010_itadopt {c |}{col 28}{res}{space 2}-.0539316{col 40}{space 2} .1129306{col 51}{space 1}   -0.48{col 60}{space 3}0.633{col 68}{space 4}-.2752715{col 81}{space 3} .1674083
{txt}{space 2}adoptcohort_2013_itadopt {c |}{col 28}{res}{space 2} .0318592{col 40}{space 2} .1077269{col 51}{space 1}    0.30{col 60}{space 3}0.767{col 68}{space 4}-.1792816{col 81}{space 3} .2429999
{txt}{space 2}adoptcohort_2014_itadopt {c |}{col 28}{res}{space 2} .0385409{col 40}{space 2}   .11129{col 51}{space 1}    0.35{col 60}{space 3}0.729{col 68}{space 4}-.1795835{col 81}{space 3} .2566652
{txt}{space 2}adoptcohort_2015_itadopt {c |}{col 28}{res}{space 2}-.0628347{col 40}{space 2} .1073172{col 51}{space 1}   -0.59{col 60}{space 3}0.558{col 68}{space 4}-.2731726{col 81}{space 3} .1475032
{txt}{space 2}adoptcohort_2016_itadopt {c |}{col 28}{res}{space 2} .0423519{col 40}{space 2} .1093171{col 51}{space 1}    0.39{col 60}{space 3}0.698{col 68}{space 4}-.1719056{col 81}{space 3} .2566095
{txt}{space 2}adoptcohort_2017_itadopt {c |}{col 28}{res}{space 2}-.0069869{col 40}{space 2} .1018451{col 51}{space 1}   -0.07{col 60}{space 3}0.945{col 68}{space 4}-.2065996{col 81}{space 3} .1926257
{txt}{space 2}adoptcohort_2018_itadopt {c |}{col 28}{res}{space 2}-.0835516{col 40}{space 2} .1051407{col 51}{space 1}   -0.79{col 60}{space 3}0.427{col 68}{space 4}-.2896235{col 81}{space 3} .1225204
{txt}{space 2}adoptcohort_2020_itadopt {c |}{col 28}{res}{space 2} .0180939{col 40}{space 2} .1111477{col 51}{space 1}    0.16{col 60}{space 3}0.871{col 68}{space 4}-.1997516{col 81}{space 3} .2359393
{txt}{space 2}adoptcohort_2021_itadopt {c |}{col 28}{res}{space 2}-.1774809{col 40}{space 2} .1073413{col 51}{space 1}   -1.65{col 60}{space 3}0.098{col 68}{space 4} -.387866{col 81}{space 3} .0329042
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. *
. *
. 
. ** COMPUTE PSEUDO R^2 [SSE / (SSE + SSR) = EXPLAINED/PREDICTED SUM OF SQUARES / (EXPLAINED/PREDICTED SUM OF SQUARES + RESIDUAL SUM OF SQUARES)] = SSE / SST
. 
. predict predsy_m3 if e(sample)
{txt}(statistic {bf:mean} assumed; mean function)
{res}{txt}
{com}. predict residsy_m3 if e(sample), residuals
{res}{txt}(5,781 missing values generated)

{com}. 
. gen sse_m3 = predsy_m3 * predsy_m3 if e(sample)
{txt}(5,781 missing values generated)

{com}. gen ssr_m3 = residsy_m3 * residsy_m3 if e(sample)
{txt}(5,781 missing values generated)

{com}. 
. egen sum_sse_m3 = total(sse_m3) if e(sample)
{txt}(5,780 missing values generated)

{com}. egen sum_ssr_m3 = total(ssr_m3) if e(sample)
{txt}(5,780 missing values generated)

{com}. 
. gen r2_m3 = sum_ssr_m3/(sum_sse_m3 + sum_ssr_m3)
{txt}(5,780 missing values generated)

{com}. 
. sum r2_m3

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}r2_m3 {c |}{res}      6,771    .3691151           0   .3691151   .3691151
{txt}
{com}. 
. 
. * [MODEL 3: RELATIVE TYPE I PROGRAM ERROR RATE] FIGURE 4D: UNCONDITIONAL ADAPTATION EFFECTS --  E(Y) [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]
. 
. margins, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:6,770}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .3112053{col 26}{space 2} .0072546{col 37}{space 1}   42.90{col 46}{space 3}0.000{col 54}{space 4} .2969867{col 67}{space 3}  .325424
{txt}{space 10}2  {c |}{col 14}{res}{space 2}  .309328{col 26}{space 2} .0067145{col 37}{space 1}   46.07{col 46}{space 3}0.000{col 54}{space 4} .2961678{col 67}{space 3} .3224882
{txt}{space 10}3  {c |}{col 14}{res}{space 2}  .300436{col 26}{space 2} .0048116{col 37}{space 1}   62.44{col 46}{space 3}0.000{col 54}{space 4} .2910055{col 67}{space 3} .3098665
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .2908008{col 26}{space 2} .0048309{col 37}{space 1}   60.20{col 46}{space 3}0.000{col 54}{space 4} .2813325{col 67}{space 3} .3002692
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .2822129{col 26}{space 2} .0064763{col 37}{space 1}   43.58{col 46}{space 3}0.000{col 54}{space 4} .2695197{col 67}{space 3} .2949062
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .2745854{col 26}{space 2} .0083963{col 37}{space 1}   32.70{col 46}{space 3}0.000{col 54}{space 4}  .258129{col 67}{space 3} .2910419
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .2678315{col 26}{space 2} .0101438{col 37}{space 1}   26.40{col 46}{space 3}0.000{col 54}{space 4}   .24795{col 67}{space 3}  .287713
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .2618642{col 26}{space 2} .0116189{col 37}{space 1}   22.54{col 46}{space 3}0.000{col 54}{space 4} .2390916{col 67}{space 3} .2846368
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .2565967{col 26}{space 2}  .012817{col 37}{space 1}   20.02{col 46}{space 3}0.000{col 54}{space 4} .2314758{col 67}{space 3} .2817176
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .2519422{col 26}{space 2} .0137653{col 37}{space 1}   18.30{col 46}{space 3}0.000{col 54}{space 4} .2249628{col 67}{space 3} .2789217
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .2478138{col 26}{space 2} .0145037{col 37}{space 1}   17.09{col 46}{space 3}0.000{col 54}{space 4}  .219387{col 67}{space 3} .2762406
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .2441246{col 26}{space 2} .0150786{col 37}{space 1}   16.19{col 46}{space 3}0.000{col 54}{space 4} .2145712{col 67}{space 3} .2736781
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) ///
> legend(on order(1 "Unconditional Adaptation") pos(6) ring(2) cols(2) size(9pt))  ///
> title(" {c -(}bf:FIGURE 4D{c )-}""{c -(}bf:Unconditional Adaptation Effect{c )-}" "{c -(}bf:(Relative Type I Program Error Rate [MODEL 3]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+2 r-6)) ///
> xtitle("Months since IT Reform", size(10pt) margin(t+2 b+2)) ///
> ytitle("Relative Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{res}{txt}
{com}. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model 3.FIGURE 4D.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model 3.FIGURE 4D.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model 3.FIGURE 4D.04-10-2025.gph} saved

{com}. 
. *
. *
. *
. *
. * [MODEL 3: RELATIVE TYPE I PROGRAM ERROR RATE] FIGURE 4E: MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (relt1_interstate_cat==2) & LOW COMPLEXITY (relt1_interstate_cat==0) VALUES [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]: ***
. 
. margins r.relt1_interstate_cat if relt1_interstate_cat==0|relt1_interstate_cat==2, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:3,457}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 12}{hline 11}
{col 26}{text}{c |}         df{col 38}        chi2{col 50}     P>chi2
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 12}{hline 11}
relt1_interstate_cat@_at {c |}
{space 12}(2 vs 0)  1  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     2.56{col 50}{space 2}   0.1097
{txt}{space 12}(2 vs 0)  2  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     2.76{col 50}{space 2}   0.0965
{txt}{space 12}(2 vs 0)  3  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     3.24{col 50}{space 2}   0.0717
{txt}{space 12}(2 vs 0)  4  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     2.72{col 50}{space 2}   0.0993
{txt}{space 12}(2 vs 0)  5  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     2.03{col 50}{space 2}   0.1539
{txt}{space 12}(2 vs 0)  6  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     1.57{col 50}{space 2}   0.2107
{txt}{space 12}(2 vs 0)  7  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     1.27{col 50}{space 2}   0.2601
{txt}{space 12}(2 vs 0)  8  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     1.07{col 50}{space 2}   0.3017
{txt}{space 12}(2 vs 0)  9  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     0.92{col 50}{space 2}   0.3382
{txt}{space 12}(2 vs 0) 10  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     0.79{col 50}{space 2}   0.3733
{txt}{space 12}(2 vs 0) 11  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     0.68{col 50}{space 2}   0.4113
{txt}{space 12}(2 vs 0) 12  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     0.55{col 50}{space 2}   0.4568
{col 1}{text}                  Joint {col 26}{c |}{result}  (not testable)
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 26}{c |}{col 38} Delta-method
{col 26}{c |}   Contrast{col 38}   std. err.{col 50}     [95% con{col 63}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
relt1_interstate_cat@_at {c |}
{space 12}(2 vs 0)  1  {c |}{col 26}{res}{space 2} -.020335{col 38}{space 2} .0127122{col 49}{space 5}-.0452504{col 63}{space 3} .0045804
{txt}{space 12}(2 vs 0)  2  {c |}{col 26}{res}{space 2}-.0204504{col 38}{space 2}  .012306{col 49}{space 5}-.0445697{col 63}{space 3} .0036689
{txt}{space 12}(2 vs 0)  3  {c |}{col 26}{res}{space 2}-.0209329{col 38}{space 2} .0116232{col 49}{space 5} -.043714{col 63}{space 3} .0018483
{txt}{space 12}(2 vs 0)  4  {c |}{col 26}{res}{space 2} -.021301{col 38}{space 2} .0129249{col 49}{space 5}-.0466334{col 63}{space 3} .0040314
{txt}{space 12}(2 vs 0)  5  {c |}{col 26}{res}{space 2}-.0214346{col 38}{space 2} .0150317{col 49}{space 5}-.0508961{col 63}{space 3}  .008027
{txt}{space 12}(2 vs 0)  6  {c |}{col 26}{res}{space 2}-.0213289{col 38}{space 2} .0170401{col 49}{space 5} -.054727{col 63}{space 3} .0120691
{txt}{space 12}(2 vs 0)  7  {c |}{col 26}{res}{space 2}-.0209794{col 38}{space 2} .0186305{col 49}{space 5}-.0574946{col 63}{space 3} .0155358
{txt}{space 12}(2 vs 0)  8  {c |}{col 26}{res}{space 2}-.0203812{col 38}{space 2} .0197351{col 49}{space 5}-.0590613{col 63}{space 3} .0182989
{txt}{space 12}(2 vs 0)  9  {c |}{col 26}{res}{space 2}-.0195297{col 38}{space 2} .0203915{col 49}{space 5}-.0594964{col 63}{space 3}  .020437
{txt}{space 12}(2 vs 0) 10  {c |}{col 26}{res}{space 2}-.0184201{col 38}{space 2} .0206892{col 49}{space 5}-.0589701{col 63}{space 3}   .02213
{txt}{space 12}(2 vs 0) 11  {c |}{col 26}{res}{space 2}-.0170476{col 38}{space 2} .0207476{col 49}{space 5}-.0577122{col 63}{space 3} .0236169
{txt}{space 12}(2 vs 0) 12  {c |}{col 26}{res}{space 2}-.0154077{col 38}{space 2} .0207049{col 49}{space 5}-.0559886{col 63}{space 3} .0251732
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
> title(" {c -(}bf:FIGURE 4E{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity{c )-}" "{c -(}bf:(Interstate Claims: Relative Type I Program Error Rate [MODEL 3]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+1 r-6)) ///
> xtitle("Months since IT Reform", size(10pt) margin(t+2 b+2)) ///
> ytitle("Relative Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model 3.FIGURE 4E.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model 3.FIGURE 4E.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model 3.FIGURE 4E.04-10-2025.gph} saved

{com}. *
. *
. *
. *
. *
. 
. * [MODEL 3: RELATIVE TYPE I PROGRAM ERROR RATE] FIGURE 4F: MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (relt1_diffoccupseek_cat==2) & LOW COMPLEXITY (relt1_extbenefits_cat==0) VALUES [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]: ***
. 
. margins r.relt1_diffoccupseek_cat if relt1_diffoccupseek_cat==0|relt1_diffoccupseek_cat==2, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:3,391}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 28}{c TT}{hline 11}{hline 12}{hline 11}
{col 29}{text}{c |}         df{col 41}        chi2{col 53}     P>chi2
{res}{col 1}{text}{hline 28}{c +}{hline 11}{hline 12}{hline 11}
relt1_diffoccupseek_cat@_at {c |}
{space 15}(2 vs 0)  1  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     3.96{col 53}{space 2}   0.0465
{txt}{space 15}(2 vs 0)  2  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     3.79{col 53}{space 2}   0.0515
{txt}{space 15}(2 vs 0)  3  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     2.22{col 53}{space 2}   0.1362
{txt}{space 15}(2 vs 0)  4  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     0.72{col 53}{space 2}   0.3963
{txt}{space 15}(2 vs 0)  5  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     0.21{col 53}{space 2}   0.6481
{txt}{space 15}(2 vs 0)  6  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     0.07{col 53}{space 2}   0.7948
{txt}{space 15}(2 vs 0)  7  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     0.03{col 53}{space 2}   0.8590
{txt}{space 15}(2 vs 0)  8  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     0.03{col 53}{space 2}   0.8675
{txt}{space 15}(2 vs 0)  9  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     0.04{col 53}{space 2}   0.8358
{txt}{space 15}(2 vs 0) 10  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     0.08{col 53}{space 2}   0.7724
{txt}{space 15}(2 vs 0) 11  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     0.17{col 53}{space 2}   0.6844
{txt}{space 15}(2 vs 0) 12  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     0.31{col 53}{space 2}   0.5806
{col 1}{text}                     Joint {col 29}{c |}{result}  (not testable)
{col 1}{text}{hline 28}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 29}{c |}{col 41} Delta-method
{col 29}{c |}   Contrast{col 41}   std. err.{col 53}     [95% con{col 66}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
relt1_diffoccupseek_cat@_at {c |}
{space 15}(2 vs 0)  1  {c |}{col 29}{res}{space 2} .0231714{col 41}{space 2} .0116392{col 52}{space 5}  .000359{col 66}{space 3} .0459837
{txt}{space 15}(2 vs 0)  2  {c |}{col 29}{res}{space 2} .0218769{col 41}{space 2}  .011235{col 52}{space 5}-.0001432{col 66}{space 3}  .043897
{txt}{space 15}(2 vs 0)  3  {c |}{col 29}{res}{space 2} .0161405{col 41}{space 2} .0108308{col 52}{space 5}-.0050875{col 66}{space 3} .0373685
{txt}{space 15}(2 vs 0)  4  {c |}{col 29}{res}{space 2} .0107913{col 41}{space 2} .0127217{col 52}{space 5}-.0141429{col 66}{space 3} .0357254
{txt}{space 15}(2 vs 0)  5  {c |}{col 29}{res}{space 2} .0069851{col 41}{space 2} .0153037{col 52}{space 5}-.0230095{col 66}{space 3} .0369798
{txt}{space 15}(2 vs 0)  6  {c |}{col 29}{res}{space 2} .0045834{col 41}{space 2} .0176258{col 52}{space 5}-.0299625{col 66}{space 3} .0391294
{txt}{space 15}(2 vs 0)  7  {c |}{col 29}{res}{space 2} .0034474{col 41}{space 2} .0194084{col 52}{space 5}-.0345923{col 66}{space 3} .0414871
{txt}{space 15}(2 vs 0)  8  {c |}{col 29}{res}{space 2} .0034385{col 41}{space 2} .0206159{col 52}{space 5} -.036968{col 66}{space 3}  .043845
{txt}{space 15}(2 vs 0)  9  {c |}{col 29}{res}{space 2} .0044179{col 41}{space 2} .0213092{col 52}{space 5}-.0373473{col 66}{space 3} .0461831
{txt}{space 15}(2 vs 0) 10  {c |}{col 29}{res}{space 2} .0062471{col 41}{space 2} .0215971{col 52}{space 5}-.0360823{col 66}{space 3} .0485766
{txt}{space 15}(2 vs 0) 11  {c |}{col 29}{res}{space 2} .0087874{col 41}{space 2}   .02162{col 52}{space 5}-.0335871{col 66}{space 3} .0511619
{txt}{space 15}(2 vs 0) 12  {c |}{col 29}{res}{space 2} .0119001{col 41}{space 2} .0215411{col 52}{space 5}-.0303196{col 66}{space 3} .0541198
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
> title(" {c -(}bf:FIGURE 4F{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity{c )-}" "{c -(}bf:(Seeking Different Occupation: Relative Type I Program Error Rate [MODEL 3]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+1 r-6)) ///
> xtitle("Months since IT Reform", size(10pt) margin(t+2 b+2)) ///
> ytitle("Relative Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model 3.FIGURE 4F.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model 3.FIGURE 4F.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model 3.FIGURE 4F.04-10-2025.gph} saved

{com}. 
. 
. 
. 
. 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. 
. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\OUTPUT\Performance Management.MANUSCRIPT MODELS.04-10-2025.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}10 Apr 2025, 23:14:37
{txt}{.-}
{smcl}
{txt}{sf}{ul off}